Point of Sale (POIT) System

Abstract

This invention provides a system and method for performing item detection at the point of sale. [Solution] In one exemplary embodiment, the method is performed by a POS system device having a terminal station device and a bagging station device. The terminal station device includes an optical scanner. The bagging station device includes a bagging area. Furthermore, the POS system device is operably coupled to an optical sensor device having a field of view including an area around the POS system device, and the POS area has a set of POS sub-regions. The method includes the step of applying an artificial intelligence model to a set of interaction object tracking characteristics to enable the determination that one of the set of detected objects has been transported to a POS sub-region associated with the bagging area without being scanned.

Description

Technical Field

[0001] Embodiments of the present invention relate to an article detection point-of-sale information management system.

Background Art

[0002]

[0001] Retailers use a point-of-sale (POS) hardware and software system to streamline the checkout operation, enabling the retailer to process sales, handle payments, and store transactions for later retrieval. Each POS system generally includes several components, including a POS terminal station and a POS bagging station. The POS bagging station can enable a customer or retail staff to bag the retail items purchased during checkout in the POS system. The POS terminal station device can include a computer, monitor, cash drawer, receipt printer, customer display, barcode scanner, or debit / credit card reader. The POS system can also include a conveyor belt, checkout divider, weighing scale, integrated credit card processing system, signature capture device, or customer PIN pad device. While the POS system can include a keyboard and mouse, more and more POS systems are coming to include a monitor with touch screen technology. Additionally, the software integrated with the POS system can be configured to handle numerous customer-based functions such as product scanning, sales, returns, exchanges, reserved purchases, gift cards, gift registries, customer loyalty programs, promotions, and discounts. In a retail environment, there can be multiple POS systems communicating with a server via a network.

Summary of the Invention

Problems to be Solved by the Invention

[0003] The problem to be solved by the present invention is to provide a system and method for performing article detection at the point of sale.

Means for Solving the Problems

[0004] The method of the embodiment is a method performed on a point-of-sale (POS) system device having a terminal station device and a bagging station device with a bagging area, wherein the terminal station device includes a scan platform having a scan window and an optical scanner operable to scan through the scan window for object identifier codes attached to an object while being moved over the scan window, the POS system device is operably coupled to an optical sensor device that captures images of a POS area including a set of POS sub-areas having a POS sub-area associated with a container configured to carry an object, a POS sub-area associated with the scan window and a POS sub-area associated with the bagging area, and the method includes the steps of: obtaining sequential image data, which is a set of sequential images of the POS area corresponding to each sequential position of the object, captured by the optical sensor device as the object moves within the POS area; and determining whether the object has been moved to the POS sub-area associated with the bagging area without being scanned, based on a predetermined set of criteria associated with at least one of the set of POS sub-areas and an object movement track having a set of sequential object positions of the object as the object moves within the POS area. [Brief explanation of the drawing]

[0005]

[0002] The present disclosure will now be described in full by reference to the accompanying drawings illustrating embodiments of the present disclosure. However, the present disclosure should not be construed as being limited to the embodiments described herein. Rather, these embodiments are provided so as to convey the scope of the present disclosure to those skilled in the art, making it thorough and complete. Similar numbers refer to similar elements throughout.

[0006] [Figure 1] Figure 1 shows one embodiment of a POS system that can be operated to perform item detection according to the various embodiments described herein. [Figure 2A] Figure 2A shows another embodiment of a POS system device or optical sensor device according to various aspects described herein. [Figure 2B] Figure 2B shows another embodiment of a POS system device or optical sensor device according to various aspects described herein. [Figure 3] Figure 3 shows another embodiment of a POS system device or optical sensor device according to various aspects described herein. [Figure 4A] Figure 4A shows an embodiment of a method performed by a POS system device or optical sensor device that performs item detection according to various embodiments described herein. [Figure 4B] Figure 4B shows an embodiment of a method performed by a POS system device or optical sensor device that performs article detection according to various embodiments described herein. [Figure 4C] Figure 4C shows an embodiment of a method performed by a POS system device or optical sensor device that performs article detection according to various embodiments described herein. [Figure 4D] Figure 4D shows an embodiment of a method performed by a POS system device or optical sensor device that performs article detection according to various embodiments described herein. [Figure 5] Figure 5 shows another embodiment of a POS system device or optical sensor device according to various aspects described herein. [Modes for carrying out the invention]

[0007]

[0008] For the sake of brevity and illustrative purposes, this disclosure is described primarily by reference to its exemplary embodiments. Numerous specific details are provided in the following description to provide a complete understanding of the disclosure. However, it will be readily apparent to those skilled in the art that this disclosure can be implemented without being limited to these specific details.

[0008]

[0009] Self-checkout stations can utilize weight-based item security to ensure that consumers place scanned items into shopping carts or bags. Furthermore, computer vision systems can capture video of activity associated with the self-checkout station and analyze consumer interactions and behavior based on the captured video. In addition, specific models and algorithms can be integrated at different stages in processing the captured video. These models and algorithms can extract useful information from the captured video and process it to represent various stages of consumer interaction with the self-checkout terminal. For example, a computer vision system can be used to detect when a consumer places scanned or weighed items into the bagging area. The computer vision system can also detect when unscanned or unweighed items are being moved to the bagging area and, in response, generate a warning indicating potentially fraudulent activity. Thus, computer vision systems can be configured to evaluate specific consumer behaviors at self-checkout stations to improve the detection of both legitimate and potentially fraudulent consumer activity.

[0009]

[0010] In this disclosure, embodiments described herein may include the use of a computer vision system to track a target object (e.g., retail goods, hands, coin purses, smartphones, carts, baskets, plastic bags) around a POS system (e.g., a self-checkout station, a payment station) from a specific starting point (e.g., a cart, a basket) to a specific ending point (e.g., a bagging area), and may also include tracking the trajectory of the target object. Once tracking of the target object around the POS system is complete, the processing circuit configuration of the POS system or computer vision system may, based on heuristics, evaluate a set of rules or criteria to verify that the tracking of the target object corresponds to a “cart-bag” scenario in which the object is transferred from the cart or basket to the bagging area of ​​the POS system without being scanned. If the evaluation indicates a “cart-bag” scenario, the target object is identified as having been transferred to the bagging area without being scanned.Rules or criteria for identifying that a target object has been transferred to the bagging area without being scanned include, namely, that the target object has been scanned more than once by the POS system, that the target object has been scanned by the POS system's portable scanning device, that the target object has entered fewer than two POS sub-regions (e.g., bagging area, container area, scanning platform, scanning window) within the area surrounding the POS system, that the target object has performed fewer than two steps within a POS sub-region, that the maximum distance between the target object track and the sub-region associated with the bagging area is less than a certain distance threshold, that the end POS sub-region of the target object track is not a POS sub-region associated with the bagging area, that the start POS sub-region of the target object track is a POS sub-region associated with the bagging area, that the duration from when the target object starts in any POS sub-region until it enters a sub-region associated with the bagging area is less than a certain duration threshold, that the target object track corresponds to a POS sub-region associated with the scanning window, that the area of ​​the target object displayed in each sequential image is less than a certain area threshold associated with an object having a certain minimum size, that the duration for which the target object has at least a certain minimum area is at least a certain duration threshold, and similar, or any combination thereof.

[0010]

[0011] In another exemplary embodiment, once tracking of a target object around the POS system is complete, the processing circuit configuration of the POS system or computer vision system can generate statistics associated with the tracking of the target object, extract features from those statistics, and apply a machine learning model to the extracted features to obtain the probability that the target object will be transferred from the shopping cart to the bagging area without being scanned. The statistics associated with the tracking of the target object and the resulting extracted features relate to the area around the POS system, as well as objects detected within POS subregions within the POS area, such as POS subregions associated with containers (e.g., shopping carts, shopping bags, shopping baskets), POS subregions associated with scan windows, and / or POS subregions associated with bagging areas.

[0011]

[0012] Figure 1 shows one embodiment of a POS system 100 that can operate to perform point-of-sale item detection according to various embodiments described herein. As shown in Figure 1, the POS system 100 (e.g., checkout station device, self-checkout station device) can be communicably connected to a network node (e.g., server) via a network (e.g., Ethernet, WiFi, Internet). The POS system 100 may include a terminal station device 102 and a bagging station device 141. The terminal station device 102 may include a housing 112, a scanning platform 114 having a scanner window 115 through which an optical scanner device disposed below the scanner window 115 can scan visual object identifier codes (e.g., barcodes, QR codes®) disposed on, above, or around the scanner window 115, another optical scanner 116 (e.g., a portable or handheld scanner), a display device 118 (e.g., a touchscreen), a payment processing mechanism 122 (e.g., a credit card transaction device), a printer 124, a coupon slot mechanism 125, a cash receiving mechanism 126, a change (e.g., coins, cash) interface mechanism 128, and so on, or any combination thereof. In addition, the terminal station device 102 may be configured to include a set of light-emitting element (LED) devices 130a to e (collectively, LED device 130). The housing 112 may be configured to include a cabinet that houses a processing circuit configuration that can operate to control the operation and functions of the POS system 100. Each LED device 130a to e may be configured to be individually or collectively controlled by the processing circuit of the POS system 100 to display specific contextual information to a consumer or retail store employee.Although not expressly shown herein, the housing 112 may also house cabling and other functional components for connecting the POS system 100 to a network (e.g., Ethernet, WiFi, the Internet) or a network node (e.g., a server) via the network, or for connecting the terminal station device 102 to the bagging station device 141. The bagging station device 141 may include a bagging area 143 associated with a load sensor device capable of measuring the weight of any object placed within the bagging area 143.

[0012]

[0013] In Figure 1, each scanner device 115, 116 can be configured as an optical scanner device capable of scanning a visual object identifier code (e.g., a barcode, a QR code) placed on an object 151a, b (e.g., a retail item) that a consumer intends to purchase through the POS system 100. Scanner device 116 can be configured as a handheld, battery-operated scanner that can be removed from its battery charging dock and used by the consumer or store clerk to scan a barcode on a retail item, without requiring the retail item to be removed from a shopping cart. Each visual object identifier code can represent one of a set of object identifiers (e.g., a UPC), each identifier unique to a particular object (e.g., a retail item, a product) and represented by a series of characters (e.g., numbers, alphabetic characters, alphanumeric characters). A Uniform Product Code (UPC), which may point to a UPC-A, consists of a 12-character sequence (e.g., 12 digits) uniquely assigned to each object. Along with related International Item Number (EAN) barcodes, UPCs are barcodes primarily used to scan retail goods at the point of sale, in accordance with the specifications of the International GS1 Organization. For example, a UPC-A barcode consists of a 12-character sequence (e.g., 12 digits) composed of four sections: a number system character, a 5-character serial number, a 5-character item number, and a check character.

[0013]

[0014] In Figure 1, the scanner device 115 may include a scanner window 114 and may be operable to perform dual functions as a scanner and a weighing machine to enable simultaneous scanning and weighing of retail items for consumer purchase. The scan platform 114 may be configured to allow objects to be placed on the scan platform 114 to enable weighing by the weighing function. The display 118 may be operable to display information associated with the retail items being purchased by the consumer. The payment processing mechanism 122 may consist of a PIN pad device operable to accept non-cash payment methods (e.g., credit or debit cards), while the printer 124 may be configured to print receipts or coupons. The coupon slot mechanism 125 may include an elongated slot configured to receive coupons to be used by the consumer. The cash receiving mechanism 126 may be operable to receive cash (e.g., banknotes, coins) from the consumer for the retail items being purchased by the consumer. The change interface mechanism 128 may be operable to provide change to the consumer in the form of banknotes or coins.

[0014]

[0015] Furthermore, the terminal station device 102 may also include optical sensor devices 117a-c (e.g., cameras). Each optical sensor device 117a-c may be capable of capturing an image of at least a portion of the POS system 100, capturing an image of the area around the POS system 100 including the POS area 181, capturing an image of the environment surrounding the POS system 100, and capturing an image of one or more surfaces of the POS system 100, such as the scan platform 114 or the bagging area 183. Optical sensor device 117a may have a field of view including the scan platform 114, the scan window 115, the environment in front of the POS system 100, etc. Optical sensor device 117b may have a field of view including the POS system 100, the POS area 181 around the POS system 100, the environment around the POS system 100, etc. In Figure 1, the optical sensor device 117b is shown at the end of an extension mechanism 119 (e.g., an extension pole) of the POS system 100 that extends the optical sensor device 117b above the POS system 100. In other embodiments, the optical sensor device 117b may be positioned on the POS system 100, or disposed on the ceiling surface above the POS system 100. The optical sensor device 117c may be operable to capture the environment around the POS system 100, for example, to detect consumers entering and leaving the POS area 181.

[0015]

[0016] In one exemplary operation of the POS system 100 in Figure 1, the POS system 100 or optical sensor devices 117a-c can acquire data representing a set of sequential images of the POS area 181 captured by the optical sensors (e.g., cameras) of the optical sensor devices 117a-c as objects 151a-e are moved within the POS area 181, for example, the hands 151g,h of consumer 171 grasp objects 151a,b, take them out of container 151f (e.g., cart, basket, bag), and place them in the bagging area 143. The processing circuit configuration of the POS system 100 can receive sequential image data associated with the POS area 181 from the optical sensor devices 117a-c. Additionally or alternatively, the optical sensor devices 117a-c can receive sequential image data associated with the POS area 181 from the optical sensors. The continuous image data may include a display of the POS area 181, which includes the POS system 100, the consumer 171, and a container 151f (e.g., cart, basket, bag) adjacent to the POS system 100. The POS area 181 may include a set of POS sub-areas 185a to i.The set of POS sub-regions 185a to i includes, namely, POS sub-region 185a associated with the extended area around the scan platform 114, which includes objects that may extend outside the scan platform 114 when placed on the scan platform 114; POS sub-region 185b located within POS sub-region 185a and associated with the scan platform 114; POS sub-region 185c located within POS sub-regions 185a and b and associated with the scan window 115; and POS sub-region 185d associated with the shelf of the POS system 100, which can be used to position the container while objects placed inside the container (e.g., basket, bag) are being scanned, for bagging objects 151a and b. This may include POS sub-areas 185e associated with bag holders 145a, b having bags 151d, e for use by consumer 171 during self-checkout; POS sub-areas 185f associated with an extended area around the bagging area 143 and POS sub-areas 185g associated with the bagging area 143, including objects that may extend outside the bagging area 143 when placed inside the bagging area 143; POS sub-areas 185h associated with container 151f; POS sub-areas 185i associated with personal objects 115c carried or worn by consumer 171 (e.g., clothing, hat, coin purse, handbag, wallet, eyewear, telephone, laptop, shopping bag, coffee, soda, returned items); and similar, or any combination thereof. Objects 151a and 151b placed within container 151f may include visual object identifier codes (e.g., barcodes, QR codes) placed on the objects 151a and 151b (e.g., retail goods), and each visual object identifier code is configured to be scanned by a scanner device to obtain an object identifier.

[0016]

[0017] Furthermore, the POS system 100 or optical sensor devices 117a-c can apply preprocessing to the data of each sequential image. For example, the POS system 100 or optical sensor devices 117a-c can apply filters to the data of each sequential image for purposes such as reducing image artifacts or noise, converting color pixels to grayscale pixels, orienting the POS region 181 in the same direction, trimming the periphery of the POS region 181, changing the image resolution, improving image quality, and so on, or any combination thereof. Furthermore, the POS system 100 or optical sensor devices 117a-c can determine the periphery of the POS region 181 based on the sequential image data. In addition, the POS system 100 or optical sensor devices 117a-c can determine the periphery of any or all of the POS sub-regions 185a-i. For example, the POS system 100 can define the POS region 181 or any POS sub-regions 185a-i for each sequential image based on the sequential image data. The POS system 100 or optical sensor devices 117a-c can determine the positions of objects 151a-h within the POS region 181 based on the sequential image data for each sequential image. The POS system 100 or optical sensor devices 117a-c can detect activity in one of the set of POS sub-regions 185a-i based on the sequential image data. The POS system 100 or optical sensor devices 117a-c can then determine that the detected activity in the POS sub-region 185a-i corresponds to the objects 151a-h within that POS sub-region 185a-i. Furthermore, the POS system 100 or optical sensor devices 117a-c can identify objects 151a-h as initiating an object movement track in the identified POS sub-region 185a-i. The object movement track may include a set of sequential positions of objects 151a-h as they are moved within the POS region 181 based on the sequential image data, with each sequential position associated with a corresponding sequential image.In one example, the POS system 100 or optical sensor devices 117a-c may determine that detected activity in the POS sub-area 185h corresponds to an object 151a (e.g., a retail item) placed inside a container 151f (e.g., a shopping cart) being removed from that container 151f. In another example, the POS system 100 or optical sensor devices 117a-c may determine that detected activity in the POS sub-area 185i corresponds to an object 153 (e.g., a coin purse) being removed from the shoulder of a consumer 171. In yet another example, the POS system 100 or optical sensor devices 117a-c may determine that detected activity in the POS sub-area 185c corresponds to objects 151d,e (e.g., plastic bags) being removed from their corresponding shopping bag holders 145a,b.

[0017]

[0018] Furthermore, the POS system 100 or optical sensor devices 117a-c can determine an object movement track having a set of continuous object positions of objects 151a-h as they move within the POS area 181, based on continuous image data. The POS system 100 or optical sensor devices 117a-c can identify the POS sub-areas 185a-i corresponding to the object movement tracks of objects 151a-h, based on continuous image data and the object movement track. Furthermore, the POS system 100 or optical sensor devices 117a-c can identify the target objects 151a-h as the starting or ending point of the object movement track in at least one of the set of POS sub-areas 185a-i. The POS system 100 or optical sensor devices 117a-c can determine the duration between the starting POS sub-area 185a-i and the ending POS sub-area 185a-i corresponding to the object movement track of objects 151a-h, based on continuous image data or the object movement track. The POS system 100 or optical sensor devices 117a-c can also determine the chronological order of identified POS sub-regions 185a-i based on continuous image data or object movement tracks. In addition, the POS system 100 or optical sensor devices 117a-c can determine, based on a set of criteria associated with the set of POS sub-regions 185a-i or object movement tracks, that objects 151a-h have been transferred to POS sub-regions 185f, g associated with the bagging area 143 without being scanned.The set of criteria is as follows: a first criterion associated with the determination that objects 151a-h were transported to POS sub-regions 185f and g associated with the bagging area 143 without being scanned, based on the number of POS sub-regions 185a-i corresponding to the object movement track; a second criterion associated with the determination that objects 151a-h were transported to POS sub-regions 185f and g associated with the bagging area 143 without being scanned, based on whether the POS sub-region 185c associated with the scan window 115 corresponds to the object movement track; and objects 151a-e being transported to POS sub-regions 185f and g associated with the bagging area 143 without being scanned, based on the start sub-regions 185a-i or end POS sub-regions 185a-i corresponding to the object movement track. A third criterion associated with the decision that objects 151a~e were transported to their POS sub-regions 185f,g associated with the bagging area 143 without being scanned, based on a set of consecutive positions on the object movement track and a set of distances between those sub-regions 185f,g; and a fourth criterion associated with the decision that objects 151a~e were transported to their POS sub-regions 185f,g associated with the bagging area 143 without being scanned, based on a set of consecutive positions on the object movement track and a set of distances between those sub-regions 185f,g; and a fifth criterion associated with the decision that objects 151a~e were transported to their POS sub-regions 185f,g associated with the bagging area 143 without being scanned, based on the duration between the start POS sub-regions 185a~i and end POS sub-regions 185a~i of objects 151a~e corresponding to the object movement track, similar criterion, or any combination thereof.

[0018]

[0019] In another embodiment, the POS system 100 or the optical sensor devices 117a-c can detect that the same objects 151a, b have been scanned multiple times by the optical scanning device based on continuous image data or object movement tracks. The set of criteria can further include another criterion associated with the determination that the objects 151a, b have been transferred to the POS sub-regions 185f, g associated with the bagging area 143 without being scanned, in response to the determination that the same objects 151a, b have been scanned multiple times by the optical scanning device.

[0019]

[0020] In another embodiment, the POS system 100 or the optical sensor devices 117a-c can determine that the objects 151a, b have been scanned by the portable scan device 116 based on continuous image data or object movement tracks. The set of criteria can further include another criterion associated with the determination that the objects 151a, b have been transferred to the POS sub-regions 185f, g associated with the bagging area 143 without being scanned, in response to the determination that the objects 151a, b have been scanned by the portable scan device 116.

[0020]

[0021] In another exemplary operation of the POS system 100 in Figure 1, the POS system 100 or optical sensor devices 117a-c may acquire data representing a set of sequential images of the POS area 181 captured by optical sensors 117a-c (e.g., cameras) when objects 151a-e are interacted with within the POS area 181 by the hands 151f,g, etc., of a consumer 171, for example, grasping objects 151a,b and taking them out of a container 151f (e.g., cart, basket, bag), and then transferring objects 151a,b to a bagging area 143. The processing circuit configuration of the POS system 100 may receive sequential image data associated with the POS area 181 from the optical sensor devices 117a-c. Additionally or alternatively, optical sensor devices 117a-c may receive sequential image data associated with the POS area 181 from the corresponding optical sensors. The POS system 100 or optical sensor devices 117a-c can detect, classify, or identify a set of objects 151a-h (e.g., hands, retail items, carts, baskets, coin purses, smartphones, consumers, bags, portable scanners, etc.) displayed in a set of sequential images based on sequential image data. Based on the detected set of objects and the sequential image data, the POS system 100 can detect interaction objects 151a-e that interact within the POS region 181 as displayed in the set of sequential images. Based on the sequential image data, the POS system 100 or optical sensor devices 117a-c can determine a set of sequential image segmentation masks that visually represent the segmentation of the interaction objects 151a-e displayed in the set of sequential images, the detected set of objects, and the set of POS sub-regions 185a-i. Based on the set of sequential image segmentation masks, the POS system 100 or optical sensor devices 117a-c can determine a set of detected object characteristics.The set of detected object characteristics may include information such as the detected object mask area, the distances between detected objects 151a-h (e.g., the distance between a retail item and a consumer's hand, the distance between retail items, the distance between a retail item and a shopping cart), the distance between detected objects 151a-h and POS sub-regions 185a-i, and so on, or any combination thereof. Based on the set of detected object characteristics, the POS system 100 or optical sensor devices 117a-c may extract a set of interaction object track characteristics related to the interaction with interaction objects 151a-e in the POS region 181, as displayed in a set of sequential images. The set of interaction object track characteristics may be associated with all or part of the object movement track of the interaction objects 151a-e, which, based on the sequential image data, represents a set of sequential positions of the interaction objects 151a-e as they move within the POS region 181. Furthermore, each sequential position corresponds to a specific one in the set of sequential images.

[0021]

[0022] Furthermore, the set of interaction object track characteristics includes: the duration of all or part of the object movement track of interaction objects 151a~e; the distance between interaction objects 151a~e and another detected object 151a~h; the distance between interaction objects 151a~e and POS subregions 185a~i; the distance between interaction objects 151a~e and objects 151g,f (e.g., a hand) that interact with interaction objects 151a~e; the distance between interaction objects 151a~e and container 151f; the average intersection point on POS subregions 185f,g associated with bagging area 143; the average distance moved by interaction objects 151a~e in each sequential image; the maximum distance moved by interaction objects 151a~e during interaction with interaction objects 151a~e; the distance between interaction objects 151a~e and POS subregions 185f,g associated with bagging area 143 on the first sequential image in which interaction objects 151a~e were detected; and interaction objects 151a~e and The distance between the POS subregions 185f and g associated with the bagging area 143 on the last sequence of images in which interaction objects 151a to e are detected, the percentage of sets of images in which interaction objects 151a to e are detected in the POS subregions 185f and g associated with the bagging area 143 (for example, starting from the first sequence of images in which interaction objects 151a to e are detected and ending with the last sequence of images in which interaction objects 151a to e are detected), the percentage of sets of images in which interaction objects 151a to e are detected in the POS subregions 185a to c associated with the scan platform 115 (for example, starting from the first sequence of images in which interaction objects 151a to e are detected and ending with the last sequence of images in which interaction objects 151a to e are detected), the percentage of sets of images in which interaction objects 151a to e are not detected in any of the POS subregions 185a to i (for example, starting from the first sequence of images in which interaction objects 151a to e are detected and ending with the last sequence of images in which interaction objects 151a to e are detected),The proportion of sets of consecutive images in which interaction objects 151a~e are simultaneously detected in at least two POS sub-regions 185a~c and 185f~g (for example, starting from the first consecutive image in which interaction objects 151a~e are detected and ending with the last consecutive image in which interaction objects 151a~e are detected), the proportion of sets of consecutive images in which objects 151f and g are not detected in POS region 181 (for example, starting from the first consecutive image in which interaction objects 151a~e are detected and ending with the last consecutive image in which interaction objects 151a~e are detected), and consecutive images in which interaction objects 151a~e are the only objects in the set of objects 151a~h detected in POS region 181 (for example, starting from the first consecutive image in which interaction objects 151a~e are detected, This may include the proportion of sets of consecutive images in which interaction objects 151a-e are detected (ending in the last consecutive image in which interaction objects 151a-e are detected), the proportion of sets of consecutive images in which container 151f is detected in POS region 181 (e.g., starting from the first consecutive image in which interaction objects 151a-e are detected and ending in the last consecutive image in which interaction objects 151a-e are detected), the proportion of sets of consecutive images in which interaction objects 151a,b are shown as being scanned by POS system 100 (e.g., starting from the first consecutive image in which interaction objects 151a-e are detected and ending in the last consecutive image in which interaction objects 151a-e are detected), the minimum, maximum, or average size of the mask area of ​​interaction objects 151a,b in the set of consecutive images, similar items, or any combination thereof. The set of interaction object track characteristics may include interaction object track characteristics determined over the entire object movement track of interaction objects 151a-e, specific parts of the object movement track of interaction objects 151a-e, the beginning of the object movement track of interaction objects 151a-e (e.g., the first second(s)), the end of the object movement track of interaction objects 151a-e (e.g., the last second(s)), similarly, or any combination thereof. For example,The set of interaction object track characteristics can include one or more interaction object track characteristics associated with the entire object movement tracks of interaction objects 151a - e, one or more interaction object track characteristics associated with a portion of the object movement tracks of interaction objects 151a - e corresponding to the packing area 143, and one or more interaction object track characteristics associated with the last second(s) of the object movement tracks of interaction objects 151a - e.

[0022]

[0023] The distance between interaction objects 151a - e and another detected object 151a - h can be further classified or indicated as follows: that is, only interaction objects 151a - e are detected during the interaction with interaction objects 151a - e, another object 151a - h is detected during the interaction with interaction objects 151a - e and is considered to be far away (for example, the minimum or average distance between interaction objects 151a - e and that other object 151a - h is greater than a specific distance such as 100 pixels), another object 151a - h is detected during the interaction with interaction objects 151a - e and is considered to be at a medium distance (for example, the average distance between interaction objects 151a - e and that other object 151a - h is within a specific distance range such as 50 - 100 pixels), another object 151a - h is detected during the interaction with interaction objects 151a - e and is considered to be in proximity (for example, the average distance between interaction objects 151a - e and that other object 151a - h is less than a specific distance such as 50 pixels), the same as these, or any combination thereof.

[0023]

[0024] The distance between the interaction objects 151a~e and the detected objects 151g,h associated with the hand is as follows: object 151g,h is not detected during any interaction with interaction objects 151a~e, object 151g,h is detected during interaction with interaction objects 151a~e and object 151g,h is considered to be far away (for example, the minimum or average distance between interaction objects 151a~e and object 151g,h is greater than a certain distance such as 100 pixels), object 15 Objects 151g and 151h are detected, but objects 151g and 151h are considered to be at a moderate distance (for example, the average distance between interacting objects 151a-e and object 151g and 151h is within a specific distance range such as 50-100 pixels), objects 151g and 151h are detected during interaction with interacting objects 151a-e and objects 151g and 151h are considered to be in close proximity (for example, the average distance between interacting objects 151a-e and object 151g and 151h is less than a specific distance such as 50 pixels), and so on, or any combination thereof, which can be further classified or indicated.

[0024]

[0025] The distance between interaction objects 151a~e and the detected object 151f associated with the container is as follows: object 151f was not detected during any interaction with interaction objects 151a~e, object 151f was detected during interaction with interaction objects 151a~e and object 151f is considered to be far away (for example, the minimum or average distance between interaction objects 151a~e and object 151f is greater than a certain distance such as 100 pixels), object 151f was detected during interaction with interaction objects 151a~e Object 151f is detected, but object 151f is considered to be at a moderate distance (for example, the average distance between interacting objects 151a~e and object 151f is within a specific distance range such as 50~100 pixels); object 151f is detected during interaction with interacting objects 151a~e and object 151f is considered to be in close proximity (for example, the average distance between interacting objects 151a~e and object 151f is less than a specific distance such as 50 pixels); similar cases, or any combination thereof, can be further classified or indicated.

[0025]

[0026] In this embodiment, the POS system 100 or optical sensor devices 117a-c can apply an artificial intelligence model (e.g., machine learning circuit, neural network circuit) to a set of interaction object track characteristics to obtain an indication that interaction objects 151a-e have been transported to POS sub-regions 185f, g associated with the bagging area 143 without being scanned, and the corresponding confidence level. The artificial intelligence model may include supervised learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (k-NN), naive Bayes, gradient boosting machines (e.g., XGBoost, LightGBM, CatBoost), unsupervised learning algorithms such as k-means clustering, hierarchical clustering, principal component analysis (PCA), independent component analysis (ICA), Gaussian mixture models (GMM), t-distribution stochastic nearest neighbor embedding (t-SNE), autoencoders, semi-supervised learning algorithms such as self-training, co-training, label propagation, graph-based semi-supervised learning, Q-learning, deep Q-networks (DQN), and policy gradient methods. The AI ​​can support reinforcement learning algorithms such as REINFORCE, Proximal Policy Optimization (PPO), Actor-Critic algorithms, and Monte Carlo Tree Search (MCTS), as well as deep learning algorithms such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Generative Adversarial Networks (GANs), Transformers, Autoencoders, and Attention Mechanisms, and ensemble learning algorithms such as Bagging (e.g., Bootstrap Aggregation), Boosting (e.g., AdaBoost, Gradient Boosting), Stacking, and Voting Classifiers, or any combination thereof. Furthermore, the AI ​​model can be implemented via software, firmware, circuit configurations within the POS system 100, optical sensor devices 117a-c, network nodes operably coupled to the POS system 100 via a network, or any combination thereof.In implementations including software or firmware, the processing of the corresponding portion of the artificial intelligence model can be performed across one or more processing circuits of the POS system 100 or optical sensor devices 117a-c. In implementations including circuit configurations, the processing circuit configurations of the POS system 100 or optical sensor devices 117a-c can interface with the artificial intelligence circuit configuration. In implementations where network nodes execute the artificial intelligence model, the POS system 100 can communicate with network nodes via the network, enabling the network nodes to execute the artificial intelligence model.

[0026]

[0027] Furthermore, the artificial intelligence model can be trained on a predetermined set of interaction object track characteristics related to unscanned interaction objects adjacent to POS sub-regions 185f, g associated with the bagging area 143, from the first detection to the last detection in the POS region 181. The predetermined set of interaction object track characteristics may include, i.e., a large number of data records (e.g., 100, 1,000, 10,000, 100,000 data records), each record containing the predetermined set of interaction object track characteristics, where the track represents the object in the POS region from the first detection to the last detection, each record containing aggregated detected object characteristics from at least two consecutive images, no restriction on data records based on the location where the interaction objects 151a~e were first detected, data records restricted to those where the interaction objects 151a~e were last detected adjacent to POS sub-regions 185f, g associated with the bagging area 143, similar, or any combination thereof. Next, the POS system 100 or optical sensor devices 117a-c can determine, based on the instruction and the corresponding confidence level, that the interaction objects 151a-e have been transferred to the POS sub-regions 185f, g associated with the bagging area 143 without being scanned. For example, the POS system 100 or optical sensor devices 117a-c can determine that the interaction objects 151a-e have been transferred to the POS sub-regions 185f, g associated with the bagging area 143 without being scanned if the corresponding confidence level is at least a certain confidence threshold (e.g., 50%, 75%, 80%, 85%, 90%, 95%, 98%, 99%). The POS system 100 or optical sensor devices 117a-c can send an instruction that the interaction objects 151a-e have been transferred to the POS sub-regions 185f, g associated with the bagging area 143 without being scanned.In one example, the light sensor devices 117a-c can send an instruction to the POS system 100 that the interaction objects 151a-e have been transferred to the POS sub-regions 185f, g associated with the bagging area 143 without being scanned. In another example, the POS system 100 can send an instruction to the LED devices 130a-e to enable illumination by the LED devices 130a-e to warn the store staff. In yet another example, the POS system can send an instruction to a network node that the interaction objects 151a-e have been transferred to the POS sub-regions 185f, g associated with the bagging area 143 without being scanned.

[0027]

[0028] Figure 2A shows another embodiment of the POS system device or optical sensor device 200a according to various aspects described herein. In Figure 2A, the device 200a implements various functional means, units, or modules (for example, via the processing circuit configuration 301 in Figure 3, via the processing circuit configuration 501 in Figure 5, via software code, etc.) or circuits. In one embodiment, these functional means, units, modules, or circuits (for example, for carrying out the method described herein) include, for example, an input / output interface circuit 201a operable to interface with input / output devices such as an optical sensor or optical sensor device 205a (e.g., a camera), a load sensor device 207a (e.g., a weighing scale), and an optical scanner device 209a (e.g., a camera, scanner); an image acquisition circuit 211a operable to acquire image data from the optical sensor 205a or optical scanner device 209a, etc.; an image receiving circuit 213a operable to receive instructions including continuous image data from the optical sensor 205a or optical scanner device 209a; an object detection circuit 214a operable to detect an object based on continuous image data; and a continuous position of the target object as the target object moves within the POS area based on the continuous image data. Track determination circuit 215a capable of determining an object movement track having a set of positions; continuous position determination circuit 217a capable of determining one of a set of continuous positions of a target object in a POS region based on continuous image data for each continuous image; POS sub-region identification circuit 219a capable of identifying the POS sub-regions corresponding to the object movement tracks of a target object based on continuous image data or an object movement track; start / end POS sub-region determination circuit 221a capable of identifying the start or end POS sub-region of a target object based on continuous image data or an object movement track; POS sub-region order determination circuit 223a capable of determining the chronological order of the identified POS sub-regions based on continuous image data or an object movement track;It may include a duration determination circuit 225a that can operate to determine the duration between a start POS sub-region and an end POS sub-region corresponding to an object movement track, a cart-to-bag determination circuit 227a that can operate to determine, based on continuous image data or an object movement track, that a target object has been transferred to a POS sub-region associated with a bagging area without being scanned, and / or a transmit circuit 229a that can operate to send information.

[0028]

[0029] Figure 2B shows another embodiment of the POS system device or optical sensor device 200b according to various aspects described herein. In Figure 2B, the device 200b implements various functional means, units, or modules (for example, via the processing circuit configuration 301 in Figure 3, via the processing circuit configuration 501 in Figure 5, via software code, etc.) or circuits. In one embodiment, these functional means, units, modules, or circuits (for example, for carrying out the method described herein) include, for example, an input / output interface circuit 201b operable to interface with input / output devices such as an optical sensor or optical sensor device 205b (e.g., a camera), a load sensor device 207b (e.g., a weighing scale), and an optical scanner device 209b (e.g., a camera, scanner); an image acquisition circuit 211b operable to acquire image data from the optical sensor 205b or optical scanner device 209b, etc.; an image receiving circuit 213b operable to receive instructions including continuous image data from the optical sensor 205b or optical scanner device 209b; an object detection circuit 215b operable to detect a set of detected objects based on the continuous image data; and an interacting object identified from the set of identified objects. An interaction object identification circuit 216b capable of operating in such a manner; an image mask determination circuit 217a capable of operating to determine a set of continuous image segmentation masks that visually represent the segmentation of a set of detected objects and a set of POS subregions within a set of continuous image data based on continuous image data; a detected object characteristic determination circuit 219b capable of operating to determine a set of detected object characteristics based on the set of continuous image segmentation masks; an interaction object track characteristic extraction circuit 221b capable of operating to extract a set of interaction object track characteristics related to the interaction object from the first to the last detection of that object within the POS region based on the set of detected object characteristics; an indication that the interaction object was transferred to a POS subregion associated with the bagging area without being scanned, and a corresponding confidence level to obtain,It may include an artificial intelligence circuit 223b capable of applying an artificial intelligence model to a set of interaction object characteristics 226b, an artificial intelligence training process circuit 225b capable of training an artificial intelligence model based on a predetermined set of interaction object track characteristics relating to unscanned interaction objects adjacent to a POS sub-region associated with a bagging area, from the first to the last detection in the POS region, and an unscanned object transfer determination circuit 227b capable of determining, based on instructions and corresponding confidence levels, that an interaction object has been transferred to a POS sub-region associated with a bagging area without being scanned, and / or a transmit circuit 229b capable of sending information.

[0029]

[0030] Figure 3 shows another embodiment of the POS system / device or optical sensor device 300 according to various aspects described herein. In Figure 3, the device 300 may include a processing circuit configuration 301 operably coupled to one or more of the following: a memory 303, a network communication circuit configuration 305, an optical sensor device 309 (e.g., a camera), an optical scanner device 311 (e.g., a scanner), a load sensor device 313, and so on, or any combination thereof. The network communication circuit configuration 305 is configured to transmit or receive information to or from one or more other devices via any communication technology. The processing circuit configuration 301 is configured to perform the processing described herein, such as by executing instructions stored in the memory 303. In this regard, the processing circuit configuration 301 may implement specific functional means, units, or modules. The optical sensor or optical sensor device 309 is operable to capture images, the optical scanner device 311 is operable to capture visual object identifier codes placed on objects, and the load sensor device 313 is operable to measure the load of objects.

[0030]

[0031] Figure 4A shows one embodiment of Method 400a performed by a POS system 100, 200, 300, 500 or optical sensor device 117b, 200, 300, 500 performing point-of-sale item detection according to various embodiments described herein. In Figure 4A, Method 400a may begin, for example, in block 401a, where the Method may include the step of acquiring data representing a set of sequential images of the POS area captured by the optical sensor as the object of interest moves within the POS area. For example, in block 403a, Method 400a may include the step of receiving sequential image data from the optical sensor of the optical sensor device 117b, 200, 300, 500 by the processing circuit of the POS system 100, 200, 300, 500 or optical sensor device 117b, 200, 300, 500. In block 404a, Method 400a may include the step of detecting the object of interest based on the sequential image data. In block 405a, method 400a may include the step of determining an object movement track having a set of sequential positions of the object as it moves within the POS area, based on sequential image data. For example, as represented by block 407a, method 400a may include the step of determining the position of the object in the POS area for each sequential image based on the corresponding sequential image data. In block 409a, method 400a may include the step of identifying at least one of a set of POS sub-regions corresponding to the object movement track of the object of the object, based on sequential image data or the object movement track. Method 400a may also include the step of identifying a start or end POS sub-region of the object of the object, based on sequential image data or the object movement track, as represented by block 411a. In block 413a, method 400a may include the step of determining the chronological order of the identified POS sub-regions, based on sequential image data or the object movement track.In addition, in block 415a, method 400a may include the step of determining the duration between a start POS sub-region and an end POS sub-region corresponding to an object movement track, based on continuous image data or an object movement track. In block 417a, method 400a includes the step of determining, based on continuous image data or an object movement track, that the target object has been moved to a POS sub-region associated with a bagging area without being scanned. In block 419a, method 400a may include the step of sending an instruction that the target object has been moved to a POS sub-region associated with a bagging area without being scanned.

[0031]

[0032] Figure 4B shows another embodiment of Method 400b performed by POS systems 100, 200, 300, 500 or optical sensor devices 117b, 200, 300, 500 that perform point-of-sale item detection according to various embodiments described herein. In Figure 4B, Method 400b may begin, for example, in block 401b, where the Method may include the step of detecting activity in one of a set of POS subregions based on sequential image data. In block 403b, Method 400b may include the step of determining, based on sequential image data, that the detected activity in the POS subregion corresponds to a target object. In block 405b, Method 400b may include the step of identifying the target object as one that starts or ends an object movement track in the identified POS subregion.

[0032]

[0033] Figure 4C shows another embodiment of Method 400c performed by POS systems 100, 200, 300, 500 or optical sensor devices 117b, 200, 300, 500 that perform point-of-sale item detection according to various embodiments described herein. In Figure 4C, Method 400c may begin, for example, in block 401c, where the Method may include the step of determining that the object of interest has been transported to a POS sub-region associated with a bagging area without being scanned, based on the number of POS sub-regions corresponding to the object movement track. In block 403c, Method 400c may include the step of determining that the object of interest has been transported to a POS sub-region associated with a bagging area without being scanned, based on whether the POS sub-region associated with the scan window corresponds to the object movement track. In block 405c, Method 400c may include the step of determining that the object of interest has been transported to a POS sub-region associated with a bagging area without being scanned, based on the starting or ending POS sub-regions corresponding to the object movement track. Method 400c may include the step of determining a set of distances between an object movement track and a POS subregion associated with a bagging area, as represented by block 407c. In addition, in block 409c, Method 400c may include the step of determining, based on the set of distances between an object movement track and a POS subregion associated with a bagging area, that the object in question was transported to a POS subregion associated with a bagging area without being scanned. In block 411c, Method 400c may include the step of determining, based on the chronological order of the identified POS subregions, or the time period between a start POS subregion and an end POS subregion corresponding to an object movement track, that the object in question was transported to a POS subregion associated with a bagging area without being scanned.

[0033]

[0034] Figure 4D shows one embodiment of Method 400d performed by a POS system 100, 200, 300, 500 or optical sensor device 117b, 200, 300, 500 performing point-of-sale item detection according to various embodiments described herein. In Figure 4D, Method 400d may begin, for example, in block 401d, where the Method may include the step of acquiring data representing a set of sequential images of the POS area captured by the optical sensor while at least one of the set of detected objects is interacting within the POS area. For example, Method 400d may include the step of receiving sequential image data from the optical sensor of the optical sensor device 117b, 200, 300, 500 by a processing circuit of the POS system 100, 200, 300, 500 or optical sensor device 117b, 200, 300, 500. In block 403d, Method 400d may include the step of detecting a set of detected objects displayed in the set of sequential images based on the sequential image data. In block 405d, method 400d includes the step of identifying at least one of a set of detected objects interacting within a POS region based on sequential image data in order to acquire an interaction object. In block 407d, method 400d may include the step of determining a set of sequential image segmentation masks that visually represent the segmentation of the set of detected objects and the set of POS sub-regions as displayed in a set of sequential images, based on sequential image data. In block 409d, method 400d may include the step of determining a set of detected object characteristics for each sequential image based on a set of sequential image segmentation masks. In block 411d, method 400d includes the step of extracting a set of interaction object track characteristics related to the interacting object from the first to the last detection of that object within the POS region, based on a set of detected object characteristics, for each sequential image.In block 413d, method 400d includes the step of applying an artificial intelligence model to a set of interaction object characteristics to obtain an instruction that an interaction object was transported to a POS sub-region associated with a bagging area without being scanned, and a corresponding confidence level. In block 415d, method 400d may include the step of training an artificial intelligence model based on a predetermined set of interaction object characteristics relating to interaction objects that are adjacent to and / or not scanned in a POS sub-region associated with a bagging area, from the first detection to the last detection in the POS region. In block 417d, method 400d may include the step of determining, based on the instruction and the corresponding confidence level, that an interaction object was transported to a POS sub-region associated with a bagging area without being scanned. In block 419d, method 400d may include the step of sending an instruction that an interaction object was transported to a POS sub-region associated with a bagging area without being scanned.

[0034]

[0035] Figure 5 shows another embodiment of a POS system device or optical sensor device (e.g., camera system) 500 according to various aspects described herein. In Figure 5, the device 500 includes a processing circuit configuration 501 operably coupled via a bus 503 to an input / output interface 505, an artificial intelligence circuit configuration 509 (e.g., a neural network circuit, a machine learning circuit), a network connection interface 511, a power supply 513, a memory 515 including a random access memory (RAM) 517, a read-only memory (ROM) 519, and a storage medium 521, a communication subsystem 531, and / or any other components, or any combination thereof. In one example, the device 500 can be operably coupled to one or more optical sensor devices via a wired communication interface (e.g., USB, Ethernet) or a wireless communication interface (e.g., WiFi, Bluetooth). Furthermore, the device 500 can be operably coupled to one or more optical sensor devices via the network connection interface 511 or the communication subsystem 531.

[0035]

[0036] The input / output interface 505 may be configured to provide a communication interface to an input device, an output device, or an input / output device. Device 500 may be configured to use an output device via the input / output interface 505. The output device may use the same type of interface port as the input device. For example, a USB port or a Bluetooth port can be used to provide input to and output from device 500. The output device may be a speaker, sound card, video card, display, monitor, printer, actuator, transducer 575 (e.g., speaker, ultrasonic emitter), emitter, smart card, another output device, or any combination thereof. Device 500 may be configured to use an input device via the input / output interface 505 to allow a user to capture information within device 500. The input device may include a scanner 561 (e.g., an optical scanner device), a touch-sensitive or presence-sensitive display 563, an optical sensor 575 (e.g., a camera), a load sensor (e.g., a weight sensor), a microphone, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smart card, and the like. A presence-sensitive display may include capacitive or resistive touch sensors for sensing user input. These sensors may include, for example, accelerometers, gyroscopes, tilt sensors, force sensors, magnetometers, light sensors or image sensors, infrared sensors, proximity sensors, microphones, ultrasonic sensors, other similar sensors, or any combination thereof.As shown in Figure 5, the input / output interface 505 can be configured to provide a communication interface to components of the POS system 100, such as a scanner associated with the scanner window 115, a scanner 116, a scale associated with the scan platform 114, a display device 118, a touchscreen 118, a payment processing mechanism 122, a printer 124, a coupon slot mechanism 125, a cash receiving mechanism 126, a light-emitting device 130, a keyboard, a keypad, a card reader, the like, or any combination thereof.

[0036]

[0037] In Figure 5, the storage medium 521 may include an operating system 523, application programs 525, data 527, similar items, or any combination thereof. In other embodiments, the storage medium 521 may include other similar types of information. A particular device may utilize all of the components shown in Figure 5, or only a subset of the components. The level of integration between components may vary from device to device. Furthermore, a particular device may include multiple instances of components such as multiple processors, memory, neural networks, network connectivity interfaces, and transceivers.

[0037]

[0038] In Figure 5, the processing circuit configuration 501 may be configured to process computer instructions and data. The processing circuit configuration 501 may be configured to implement any sequential state machine that operates to execute machine instructions stored in memory as machine-readable computer programs, such as one or more hardware-implemented state machines (e.g., in discrete logic, FPGA, ASIC, etc.), programmable logic with appropriate firmware, one or more stored programs with appropriate software, a general-purpose processor such as a microprocessor or digital signal processor (DSP), or any combination of the above. For example, the processing circuit configuration 501 may include two central processing units (CPUs). The data may be information in a form suitable for use by a computer.

[0038]

[0039] In Figure 5, the artificial intelligence circuit configuration 509 may be configured to learn to perform tasks by considering examples such as performing object detection, classification, or identification based on an image. For example, the first artificial intelligence circuit configuration may be configured to perform activity detection. Furthermore, the second artificial intelligence circuit configuration may be configured to perform object classification or identification. In Figure 5, the network connection interface 511 may be configured to provide a communication interface to the network 543a. The network 543a may include wired and / or wireless networks such as a local area network (LAN), wide area network (WAN), computer network, wireless network, telecommunications network, another similar network, or any combination thereof. For example, the network 543a may include a Wi-Fi network. The network connection interface 511 may be configured to include receiver and transmitter interfaces used to communicate with one or more other devices over the communication network according to one or more communication protocols such as Ethernet, TCP / IP, SONET, ATM, etc. The network connection interface 511 can implement receiver and transmitter functions suitable for the communication network link (e.g., optical, electrical, etc.). The transmitter and receiver functions may share circuit components, software, or firmware, or they may be implemented separately.

[0039]

[0040] The RAM 517 may be configured to interface with the processing circuit configuration 501 via the bus 503 and provide storage or a cache of data or computer instructions during the execution of software programs such as operating systems, application programs, and device drivers. The ROM 519 may be configured to provide computer instructions or data to the processing circuit configuration 501. For example, the ROM 519 may be configured to store immutable low-level system code or data for basic system functions such as basic input / output (I / O), startup, or receiving keystrokes from a keyboard, which are stored in non-volatile memory. The storage medium 521 may be configured to include memory such as RAM, ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, or flash drives. For example, the storage medium 521 may be configured to include an operating system 523, an application program 525 such as a web browser, a web application, a user interface, a browser data manager, a widget or gadget engine as described herein, or another application, and data files 527. The storage medium 521 may store any or a combination of various operating systems for use by the device 500.

[0040]

[0041] The storage medium 521 may be configured to include several physical drive units, such as independent disk redundant arrays (RAID), floppy disk drives, flash memory, USB flash drives, external hard disk drives, thumb drives, pen drives, key drives, high-density digital versatile disk (HD-DVD) optical disk drives, internal hard disk drives, Blu-ray optical disk drives, holographic digital data storage (HDDS) optical disk drives, external mini dual in-line memory modules (DIMMs), synchronous dynamic random access memory (SDRAM), external microDIMM SDRAM, smart card memory such as subscriber identification modules or removable user identification (SIM / RUIM) modules, other memories, or any combination thereof. The storage medium 521 may enable device 500 to access computer executable instructions, application programs, etc., stored in temporary or non-temporary memory media, to offload data, or to upload data. Products such as those utilizing communication systems may be tangibly embodied in the storage medium 521, which may include device-readable media.

[0041]

[0042] The processing circuit configuration 501 may be configured to communicate with network 543b using the communication subsystem 531. Networks 543a and 543b may be the same network or different networks. The communication subsystem 531 may be configured to include one or more transceivers used to communicate with network 543b. For example, the communication subsystem 531 may be configured to include one or more transceivers used to communicate with one or more remote transceivers of another device capable of wireless communication according to one or more communication protocols such as IEEE 802.11, CDMA, WCDMA®, GSM, LTE, UTRAN, WiMax, etc. Each transceiver may include a transmitter 533 and / or receiver 535 to implement appropriate transmitter or receiver functions for a RAN link (e.g., frequency allocation). Furthermore, the transmitters 533 and receivers 535 of each transceiver, circuit components, software, or firmware may be shared or implemented separately.

[0042]

[0043] In Figure 5, the communication functions of the communication subsystem 531 may include data communication, voice communication, multimedia communication, short-range communication such as Bluetooth, near-field communication, location-based communication such as the use of the Global Positioning System (GPS) for determining location, other similar communication functions, or any combination thereof. For example, the communication subsystem 531 may include cellular communication, Wi-Fi communication, Bluetooth communication, and GPS communication. The network 543b may encompass wired and / or wireless networks such as local area networks (LANs), wide area networks (WANs), computer networks, wireless networks, telecommunications networks, other similar networks, or any combination thereof. For example, the network 543b may be a cellular network, a Wi-Fi network, and / or a near-field network. The power supply 513 may be configured to supply alternating current (AC) or direct current (DC) power to the components of device 500.

[0043]

[0044] The features, benefits, and / or functions described herein may be implemented in one of the components of device 500, or they may be divided among multiple components of device 500. Furthermore, the features, benefits, and / or functions described herein may be implemented in any combination of hardware, software, or firmware. In one example, a communication subsystem 531 may be configured to include any of the components described herein. Furthermore, a processing circuit configuration 501 may be configured to communicate with any of such components via a bus 503. In another example, any of such components may be represented by program instructions stored in memory that, when executed by the processing circuit configuration 501, perform the corresponding function described herein. In yet another example, the function of any of such components may be divided between the processing circuit configuration 501 and the communication subsystem 531. In yet another example, non-computationally intensive functions of any of such components may be implemented in software or firmware, while computationally intensive functions may be implemented in hardware.

[0044]

[0045] Those skilled in the art will also understand that the embodiments described herein further include corresponding computer programs.

[0045]

[0046] A computer program, when executed on at least one processor of the device, includes instructions that cause the device to perform one of the processes described above. In this regard, a computer program may include one or more code modules corresponding to the means or units described above.

[0046]

[0047] The embodiment further includes a carrier containing such a computer program. This carrier may include one of the following: electronic signals, optical signals, radio signals, or a computer-readable storage medium.

[0047]

[0048] In this regard, embodiments of this specification also include a computer program product which, when stored in a non-temporary computer-readable (storage or recording) medium and executed by the device's processor, includes instructions that cause the device to perform the actions described above.

[0048]

[0049] Embodiments further include a computer program product that includes a portion of program code for performing any step of the embodiments herein when the computer program product is executed by a computing device. This computer program product may be stored on a computer-readable recording medium.

[0049]

[0050] Alternatively or additionally, some or all of the functions may be implemented by a state machine that does not have stored program instructions, or by one or more application-specific integrated circuits (ASICs) in which each function or several combinations of specific functions are implemented as custom logic circuits. Of course, a combination of the two methods may also be used. Furthermore, those skilled in the art will expect that, despite considerable effort and many design choices, it will be possible to easily generate such software instructions and programs and ICs with minimal experimentation, guided by the concepts and principles disclosed herein, for example, motivated by available time, current technology, and economic considerations.

[0050]

[0051] As used herein, the term “product” is intended to encompass computer programs accessible from any computing device, carrier, or medium. For example, computer-readable media may include, namely, magnetic storage devices such as hard disks, floppy disks, or magnetic strips; optical discs such as compact discs (CDs) or digital multipurpose discs (DVDs); smart cards; and flash memory devices such as cards, sticks, or key drives. Furthermore, it should be understood that carrier waves may be used to carry computer-readable electronic data, including those used when sending and receiving electronic data such as email, or when accessing computer networks such as the Internet or a local area network (LAN). Naturally, those skilled in the art will recognize that many modifications can be made to this configuration without departing from the scope or spirit of the subject matter of this disclosure.

[0051]

[0052] Next, further embodiments will be described. While at least some of these embodiments can be described as applicable in a particular context for illustrative purposes, the embodiments are equally applicable in other contexts not explicitly described.

[0052]

[0053] In one exemplary embodiment, the method is performed by a POS system having a terminal station device and a bagging station device with a bagging area. The terminal station device includes a scan platform having a scan window and an optical scanner operable to scan through the scan window a visual object identifier code placed on an object while it is being transported over the scan window. Furthermore, the POS system is operably coupled to an optical sensor device having an optical sensor that has a field of view including an area around the POS system and is operable to capture an image including the POS area. The POS area includes a set of POS sub-areas having a first POS sub-area associated with a container having one or more objects, a second POS sub-area associated with the scan platform, a third POS sub-area placed in the second POS area and associated with the scan window, and a fourth POS sub-area associated with the bagging area. The method includes the step of acquiring data representing a set of sequential images of the POS region captured by an optical sensor device as the target object moves within the POS region, in order to enable the determination that the target object has been moved to a fourth POS subregion without being scanned, based on a set of criteria associated with a set of POS subregions and an object movement track having a set of sequential object positions of the target object as the target object moves within the POS region. Furthermore, each sequential object position is determined based on the corresponding sequential image.

[0053]

[0054] In another exemplary embodiment, the image acquisition step may further include receiving continuous image data from the optical sensor by a processing circuit of a POS system or optical sensor device.

[0054]

[0055] In another exemplary embodiment, the method may further include the steps of detecting activity in a first POS sub-region based on sequential image data, determining, based on sequential image data, that the activity in the first POS sub-region corresponds to a target object located within a container, or identifying the target object as the initiation of an object movement track in the first POS sub-region.

[0055]

[0056] In another exemplary embodiment, the method may further include the steps of identifying at least one of a set of POS subregions corresponding to an object movement track of an object of interest, determining the chronological order of the identified POS subregions, or determining the duration between a start POS subregion and an end POS subregion corresponding to an object movement track.

[0056]

[0057] In another exemplary embodiment, the tracking position determination step may further include, for a set of consecutive images, determining a set of consecutive object positions of the target object in the POS area based on the consecutive image data, or determining an object movement track based on the set of consecutive object positions.

[0057]

[0058] In another exemplary embodiment, the tracking position determination step may further include determining the trajectory of the object at that position based on sequential image data.

[0058]

[0059] In another exemplary embodiment, the method may further include the step of determining, based on a set of criteria associated with a set of POS subregions and an object movement track, that the object in question has been moved to a fourth POS subregion without being scanned.

[0059]

[0060] In another exemplary embodiment, at least one of the set of criteria is associated with the number of sets of POS subregions corresponding to object movement tracks.

[0060]

[0061] In another exemplary embodiment, at least one of the set of criteria is associated with a start or end POS subregion of a set of POS subregions corresponding to an object movement track.

[0061]

[0062] In another exemplary embodiment, at least one of the set of criteria is associated with a particular one of the set of POS subregions corresponding to an object movement track.

[0062]

[0063] In one exemplary embodiment, the POS system includes a terminal station device and a bagging station device with a bagging area. The terminal station device includes a scan platform with a scan window and an optical scan device operable to scan through the scan window a visual object identifier code placed on an object while it is being transported over the scan window. The POS system is operably coupled to an optical sensor device having an optical sensor that has a field of view including an area around the POS system and is operable to capture an image including the POS area. The POS area includes a set of POS sub-areas having a first POS sub-area associated with a container having one or more objects, a second POS sub-area associated with the scan platform, a third POS sub-area placed in the second POS area and associated with the scan window, and a fourth POS sub-area associated with the bagging area. The POS system further includes a memory containing instructions executable by a processing circuit configuration, thereby the processing circuit configuration is configured to acquire data representing a set of sequential images of the POS region captured by an optical sensor device as the target object moves within the POS region, based on a set of criteria associated with a set of POS subregions and an object movement track having a set of sequential object positions of the target object as the target object moves within the POS region, in order to enable the determination that the target object has been transported to a fourth POS subregion without being scanned. Furthermore, each sequential position is associated with a specific one of the sequential images of the POS region.

[0063]

[0064] In another exemplary embodiment, the memory includes further instructions executable by a processing circuit configuration, thereby enabling the processing circuit configuration to: detect activity in a first POS sub-region based on sequential image data; determine, based on sequential image data, that the activity in the first POS sub-region corresponds to a target object located within a container; or identify the target object as the initiation of an object movement track in the first POS sub-region.

[0064]

[0065] In another exemplary embodiment, the memory includes further instructions that can be executed by the processing circuit configuration, thereby configuring the processing circuit configuration to identify at least one of a set of POS sub-regions corresponding to an object movement track.

[0065]

[0066] In another exemplary embodiment, the memory includes further instructions that can be executed by a processing circuit configuration, thereby enabling the processing circuit configuration to: detect activity in a POS area based on a sequence of images; determine, based on the sequence of images, that the detected activity in the POS area corresponds to a target object in a second sub-area; or determine, based on the sequence of images, that the target object may be in a bagging area without needing to be scanned or weighed.

[0066]

[0067] In another exemplary embodiment, the memory includes further instructions that can be executed by a processing circuit configuration, thereby configuring the processing circuit configuration to determine, for a set of consecutive images, a set of consecutive object positions of a target object in a POS area based on the consecutive image data, or to determine an object movement track based on the set of consecutive object positions.

[0067]

[0068] In another exemplary embodiment, the memory includes further instructions that can be executed by the processing circuit configuration, thereby configuring the processing circuit configuration to determine, based on a set of criteria associated with a set of POS subregions and an object movement track, that the object of interest has been transported to a fourth POS subregion without being scanned.

[0068]

[0069] In one exemplary embodiment, the POS system includes a terminal station device, a bagging station device, and an optical sensor device. The terminal station device has a scan platform including a scan window, and an optical scanner device operable to scan through the scan window a visual object identifier code placed on an object while it is being transported over the scan window. The bagging station device includes a bagging area. The optical sensor device has a field of view including an area around the POS system and includes an optical sensor operable to capture an image including the POS area. The POS area includes a set of POS sub-areas having a first POS sub-area associated with a container having one or more objects, a second POS sub-area associated with the scan platform, a third POS sub-area placed in the second POS area and associated with the scan window, and a fourth POS sub-area associated with the bagging area. The POS system further includes a processing circuit configuration and a memory containing instructions executable by the processing circuit configuration, thereby the processing circuit configuration operates to acquire data representing a set of consecutive images of the POS region captured by an optical sensor device as the target object moves within the POS region, in order to enable the determination that the target object has been transported to a fourth POS sub-region without being scanned, based on a set of criteria associated with a set of POS sub-regions and an object movement track having a set of consecutive object positions of the target object as the target object moves within the POS region. In addition, each consecutive position is associated with a specific one of the consecutive images in the set.

[0069]

[0070] In one exemplary embodiment, the method is performed by a POS system having a terminal station device and a bagging station device with a bagging area. Furthermore, the terminal station device includes a scanning platform having a scan window and an optical scanner operable to scan through the scan window a visual object identifier code placed on an object while it is being transported over the scan window. The POS system is operably coupled to an optical sensor device having an optical sensor that has a field of view including an area around the POS system and is operable to capture an image including the POS area. The POS area includes a set of POS sub-areas having a POS sub-area associated with a container configured to carry one or more objects, a POS sub-area associated with the scan window, and a POS sub-area associated with the bagging area. The method includes the steps of: identifying an interacting object from a set of detected objects within a POS area based on data representing a set of sequential images of the POS area captured by an optical sensor while the interacting object is interacting within the POS area; extracting a set of interacting object track characteristics based on a set of detected object characteristics determined from a set of sequential image segmentation masks that visually represent the segmentation of the set of detected objects and the set of POS sub-regions in the set of sequential images; and applying an artificial intelligence model to the set of interacting object track characteristics to enable the determination that the interacting object was transported to a POS sub-region associated with a bagging area without being scanned.

[0070]

[0071] In another exemplary embodiment, the method may further include the steps of applying an artificial intelligence model to a set of interaction object track characteristics to obtain an indication that an interaction object was transported to a POS sub-region associated with a bagging area without being scanned, and a corresponding confidence level, and determining, based on the indication and the corresponding confidence level, that the interaction object was transported to a POS sub-region associated with a bagging area without being scanned.

[0071]

[0072] In another exemplary embodiment, the method may further include the step of detecting a set of detected objects that appear in a set of sequential images, based on sequential image data.

[0072]

[0073] In another exemplary embodiment, the method may further include the step of determining, based on sequential image data, a set of sequential image segmentation masks that visually represent the segmentation of a set of detected objects and a set of POS subregions displayed in a set of sequential images.

[0073]

[0074] In another exemplary embodiment, the method may further include the step of determining a set of object characteristics detected based on a set of sequential image segmentation masks.

[0074]

[0075] In another exemplary embodiment, the method may further include the step of training an artificial intelligence model based on a predetermined set of interacting object track characteristics relating to unscanned objects adjacent to a POS sub-region associated with a bagging area, which have interacted within the POS region from the first detection of the object in the POS region to the last detection of the object in the POS region.

[0075]

[0076] In another exemplary embodiment, the set of detected object properties includes the distance between at least two of the detected objects.

[0076]

[0077] In another exemplary embodiment, the set of detected object characteristics includes the distance between at least one of the set of detected objects and at least one of the set of POS subregions.

[0077]

[0078] In another exemplary embodiment, the set of interaction object track properties includes the distance between an interaction object and another object interacting with it, from the first detection of the interaction object in the POS region to the last detection of the interaction object in the POS region.

[0078]

[0079] In another exemplary embodiment, the set of interaction object track properties includes the distance between the interaction object and the container from the first detection of the interaction object in the POS region to the last detection of the interaction object in the POS region.

[0079]

[0080] In one exemplary embodiment, the POS system includes a terminal station device and a bagging station device with a bagging area. Furthermore, the terminal station device includes a scanning platform having a scan window and an optical scanner operable to scan through the scan window a visual object identifier code placed on an object while it is being transported over the scan window. The POS system is operably coupled to an optical sensor device having an optical sensor that has a field of view including an area around the POS system and is operable to capture an image including the POS area. In addition, the POS area includes a set of POS sub-areas having a POS sub-area associated with a container configured to carry one or more objects, a POS sub-area associated with the scan window, and a POS sub-area associated with the bagging area. The POS system further includes a processing circuit configuration and a memory, the memory containing instructions executable by the processing circuit configuration, the processing circuit configuration being configured to: identify an interacting object from a set of detected objects in the POS area based on data representing a set of sequential images of the POS area captured by an optical sensor while the interacting object is interacting within the POS area; extract a set of interacting object track characteristics based on a set of detected object characteristics determined from a set of sequential image segmentation masks that visually represent the segmentation of the set of detected objects and the set of POS sub-regions in the set of sequential images; and apply an artificial intelligence model to the set of interacting object track characteristics to enable the determination that the interacting object was transported to a POS sub-region associated with a bagging area without being scanned.

[0080]

[0081] In another exemplary embodiment, the memory includes further instructions executable by the processing circuit configuration, thereby configuring the processing circuit configuration to apply an artificial intelligence model to a set of interaction object track characteristics to obtain an instruction that an interaction object was transported to a POS sub-region associated with a bagging area without being scanned, and a corresponding confidence level, and to determine, based on the instruction and the corresponding confidence level, that an interaction object was transported to a POS sub-region associated with a bagging area without being scanned.

[0081]

[0082] In another exemplary embodiment, the memory includes further instructions that can be executed by the processing circuit configuration, thereby configuring the processing circuit configuration to detect a set of detected objects displayed in a set of sequential images based on sequential image data.

[0082]

[0083] In another exemplary embodiment, the memory includes further instructions executable by the processing circuit configuration, which is configured to determine a set of sequential image segmentation masks that visually represent the segmentation of a set of detected objects and a set of POS subregions displayed in a set of sequential images, based on sequential image data.

[0083]

[0084] In another exemplary embodiment, the memory includes further instructions that can be executed by a processing circuit configuration, thereby configuring the processing circuit configuration to determine a set of object characteristics detected based on a set of sequential image segmentation masks.

[0084]

[0085] In another exemplary embodiment, the memory includes further instructions that can be executed by the processing circuit configuration, thereby configuring the processing circuit configuration to train an artificial intelligence model based on a predetermined set of interacting object track characteristics relating to unscanned objects adjacent to a POS sub-region associated with a bagging area, which have interacted within the POS region from the first detection of the object in the POS region to the last detection of the object in the POS region.

[0085]

[0086] In one exemplary embodiment, the POS system includes a terminal station device having a scanning platform including a scan window and an optical scanner device operable to scan through the scan window a visual object identifier code placed on an object while being transported over the scan window, an optical sensor device having a field of view including an area around the POS system and operable to capture an image including the POS area, wherein the POS area has a set of POS sub-regions including a POS sub-region associated with a container, a POS sub-region associated with the scan window, and a POS sub-region associated with the bagging area, and a processing circuit configuration and memory, wherein the memory is capable of executing instructions by the processing circuit configuration. The processing circuit configuration and memory include, which operate to identify an interacting object from a set of detected objects in a POS area based on data representing a set of sequential images of the POS area captured by an optical sensor while the interacting object is interacting within the POS area; extract a set of interacting object track characteristics based on a set of detected object characteristics determined from a set of sequential image segmentation masks that visually represent the segmentation of the set of detected objects and the set of POS sub-regions in the set of sequential images; and apply an artificial intelligence model to the set of interacting object track characteristics to enable the determination that the interacting object was transported to a POS sub-region associated with a bagging area without being scanned.

[0086]

[0087] The detailed descriptions set forth herein are merely illustrative and are not intended to limit the Disclosure or any use or application of the Disclosure. Furthermore, there is no intention to be bound by any expressions or implied theories presented in the aforementioned field of use, background art, abstract, or detailed descriptions. The Disclosure provides various examples, embodiments, etc., which may be described herein with respect to functional block elements or logical block elements. Various embodiments described herein are presented as methods, devices (or apparatus), systems, or articles, which may include several components, elements, members, modules, nodes, peripherals, etc. Furthermore, these methods, devices, systems, or articles may or may not include additional components, elements, members, modules, nodes, peripherals, etc.

[0087]

[0088] Furthermore, various embodiments described herein can be implemented using standard programming or engineering techniques to generate software, firmware, hardware (e.g., circuitry), or any combination thereof, and can control computing devices to implement the disclosed subject matter. It will be understood that some embodiments may consist of one or more general-purpose or dedicated processors, such as microprocessors, digital signal processors, customized processors, and field-programmable gate arrays (FPGAs), and a set of specific stored program instructions (including both software and firmware) that control one or more processors to perform some, most, or all of the functions of the methods, devices, and systems described herein in conjunction with specific non-processor circuits.

[0088]

[0089] Throughout this specification and its embodiments, the following terms have at least the meaning expressly related to this specification unless otherwise explicitly indicated by the context. Relational terms such as “first” and “second” may be used solely to distinguish one entity or action from another entity or action, without necessarily requiring or implying any actual relationship or order between such entities or actions. The term “or” is intended to mean comprehensive “or” unless otherwise specified or unless it is evident from the context that it refers to an exclusive form. Furthermore, the terms “a,” “an,” and “the” are intended to mean one or plural unless otherwise specified or unless it is evident from the context that they refer to a singular form. The term “including” and its various forms are intended to mean including but not limited to. References to “one embodiment,” “embodiment,” “exemplary embodiment,” “various embodiments,” and other similar terms indicate that embodiments of the disclosed technology described in such terms may include certain functions, features, structures, or characteristics, but not all embodiments necessarily include those particular functions, features, structures, or characteristics. Furthermore, repeated use of the phrase “in one embodiment” does not necessarily refer to the same embodiment, but may. The terms “substantially,” “essentially,” “approximately,” “about,” or any other version thereof are defined as close to what is understood by those skilled in the art, and in one non-limiting embodiment, this term is defined as within 10%, in another embodiment within 5%, in yet another embodiment within 1%, and in yet another embodiment within 0.5%. A device or structure “configured” in a particular way is configured in at least that way, but may be configured in ways not listed. [Explanation of Symbols]

[0089] 100 POS systems 102 Terminal Station Devices 112 Housing 114 Scan Platform 115 Scanner window, scan window, scanner device 116 Optical scanners, scanner devices, portable scanning devices 117a~c Optical sensor devices 118 Displays, display devices, touchscreens 119 Extension mechanism 122 Payment Processing Organizations 124 Printers 125 Coupon Slot Mechanism 126 Cash Acceptance Organizations 128 Change Dispensing Interface Mechanism 130 LED devices, light-emitting devices 130a~e LED devices 141 Bagging Station Device 143 Bagging Area 145a, b Bag holder 151a~h Objects 153 Personal Objects 171 Consumer 181 POS area 185a~i POS sub-area 200 POS systems 200a POS system device or optical sensor device 200b POS system device or optical sensor device 201a Input / Output Interface Circuit 201b Input / Output Interface Circuit 205a Optical sensor or optical sensor device 205b Optical sensor or optical sensor device 207a Load sensor device 207b Load sensor device 209a Optical scanner device 209b Optical scanner device 211a Image acquisition circuit 211b Image acquisition circuit 213a Image receiving circuit 213b Image receiving circuit 214a Object detection circuit 215a Track determination circuit 215b Object detection circuit 216b Interaction Object Identification Circuit 217a Continuous position determination circuit 217a Image mask determination circuit 219a POS sub-region identification circuit 219b Circuit for determining the characteristics of detected objects 221a Start / End POS Sub-region Determination Circuit 221b Interaction Object Track Characterization Circuit 223a POS sub-region ordering circuit 223b Artificial Intelligence Circuit 225a Duration Determination Circuit 225b Artificial Intelligence Training Process Circuit 227a Cart-Bag Decision Circuit 227b Unscanned Object Transfer Decision Circuit 229a Transmitting Circuit 229b Transmitter Circuit 300 POS systems / devices or optical sensor devices 301 Processing Circuit Configuration 303 memory 305 Network Communication Circuit Configuration 309 Optical Sensor Devices 311 Optical Scanner Devices 313 Load Sensor Devices 400a Method 400b method 400c method 400d method 500 POS system or optical sensor device 501 Processing Circuit Configuration Bus 503 505 Input / Output Interface 509 Artificial Intelligence Circuit Configuration 511 Network Connection Interface 513 Power supply 515 memory 517 Random Access Memory (RAM) 519 Read-only memory (ROM) 521 Storage medium 523 Operating Systems 525 Application Programs 527 Data, Data Files 531 Communication Subsystem 533 Transmitter 535 Receiver 543a Network 543b Network 561 Scanner 563 Touch-sensitive or presence-sensitive display 575 Light Sensor

Claims

1. A method performed on a point-of-sale (POS) system device having a terminal station device and a bagging station device with a bagging area, The terminal station device includes a scan platform having a scan window and an optical scanner operable to scan object identifier codes attached to objects while they are being transported over the scan window, The POS system device is operably coupled to an optical sensor device that captures an image including a POS region which includes a set of POS subregions having a POS subregion associated with a container configured to carry an object, a POS subregion associated with the scan window, and a POS subregion associated with the bagging area. The steps include acquiring continuous image data, which is a set of continuous images of the POS region corresponding to each continuous position of the target object, captured by the optical sensor device when the target object moves within the POS region, A step of determining whether the target object was transported to the POS sub-region associated with the bagging area without being scanned, based on a predetermined set of criteria associated with at least one of the set of POS sub-regions and an object movement track having a set of consecutive object positions of the target object when the target object is moved within the POS region, Methods that include...

2. The step of acquiring the aforementioned continuous image data is, The processing circuit of the POS system device or the optical sensor device receives the continuous image data from the optical sensor device. The method according to claim 1, further comprising:

3. The steps include detecting activity in the POS sub-region associated with the container based on the continuous image data, The steps include determining, based on the continuous image data, that the activity in the POS sub-region corresponds to the placement of the target object within the container, The steps include: identifying the target object as the object movement track to start in the POS sub-region; The method according to claim 1, further comprising:

4. The steps include identifying at least one of the sets of POS subregions corresponding to the object movement track of the target object, The steps include determining the duration between the start POS sub-region and the end POS sub-region corresponding to the object movement track. The method according to claim 1, further comprising:

5. The steps include determining a set of consecutive object positions for the target object within the POS region based on the continuous image data, The step of determining the object movement track based on the set of consecutive object positions. The method according to claim 4, further comprising:

6. A step of determining the trajectory of the target object at the position based on the continuous image data. The method according to claim 4, further comprising:

7. The step of determining, based on the set of predetermined criteria associated with the set of POS subregions and the object movement track, that the target object was moved to the POS subregion associated with the bagging area without being scanned. The method according to claim 1, further comprising:

8. The method according to claim 1, wherein at least one of the predetermined set of criteria is associated with the number of sets of POS subregions corresponding to the object movement track.

9. The method according to claim 1, wherein at least one of the predetermined set of criteria is associated with a start POS subregion or an end POS subregion of the set of POS subregions corresponding to the object movement track.

10. The method according to claim 1, wherein at least one of the predetermined set of criteria is associated with a particular one of the set of POS subregions corresponding to the object movement track.

11. A point-of-service (POS) system device, The POS system device comprises a terminal station device and a bagging station device with a bagging area. The terminal station device includes a scan platform having a scan window and an optical scanner operable to scan object identifier codes attached to objects while they are being transported over the scan window, The POS system device is operably coupled to an optical sensor device that captures an image including a POS region which includes a set of POS subregions having a POS subregion associated with a container configured to carry an object, a POS subregion associated with the scan window, and a POS subregion associated with the bagging area. The POS system device further includes a processing circuit configuration and a memory, the memory includes instructions that can be executed by the processing circuit configuration, and thereby the processing circuit configuration When the target object moves within the POS region, the optical sensor device acquires a set of sequential images representing the POS region. Based on a predetermined set of criteria associated with at least one of the set of POS subregions and an object movement track having a set of consecutive object positions of the target object as the target object is moved within the POS region, it is determined whether the target object was moved to the POS subregion associated with the bagging area without being scanned. POS system device.

12. The memory includes further instructions that can be executed by the processing circuit configuration, thereby enabling the processing circuit configuration Based on the continuous image data, activity in the POS sub-region associated with the container is detected, Based on the continuous image data, it is determined that the activity in the POS sub-region corresponds to the placement of the target object within the container. The aforementioned target object is identified as the object movement track to be initiated in the POS sub-region. A POS system device according to claim 11, configured to perform the following:

13. The memory includes further instructions that can be executed by the processing circuit configuration, thereby enabling the processing circuit configuration Identifying at least one of the sets of POS subregions corresponding to the object movement track of the target object, To determine the duration between the start POS subregion and the end POS subregion corresponding to the object movement track. A POS system device according to claim 11, configured to perform the following:

14. The memory includes further instructions that can be executed by the processing circuit configuration, thereby enabling the processing circuit configuration Based on the continuous image data, determine the set of continuous object positions of the target object within the POS area. Determining the object movement track based on the set of consecutive object positions. A POS system device according to claim 11, configured to perform the following:

15. The memory includes further instructions that can be executed by the processing circuit configuration, thereby enabling the processing circuit configuration Sends an instruction that the target object has been transferred to the POS sub-region associated with the bagging area without being scanned. The POS system device according to claim 11, configured as described above.

16. The memory includes further instructions that can be executed by the processing circuit configuration, thereby enabling the processing circuit configuration Based on the set of predetermined criteria associated with the set of POS subregions and the object movement track, it is determined that the target object was moved to the POS subregion associated with the bagging area without being scanned. The POS system device according to claim 11, configured as described above.

17. The POS system device according to claim 11, wherein at least one of the predetermined set of criteria is associated with the number of sets of POS subregions corresponding to the object movement track.

18. The POS system device according to claim 11, wherein at least one of the predetermined set of criteria is associated with a particular one of the set of POS subregions corresponding to the object movement track.

19. The POS system device according to claim 11, wherein at least one of the predetermined set of criteria is associated with a start POS subregion or an end POS subregion of the set of POS subregions corresponding to the object movement track.

20. A point-of-service (POS) system, A terminal station device comprising a scanning platform including a scanning window, and an optical scanner device capable of scanning object identifier codes attached to objects while they are being transported over the scanning window, A bagging station device having a bagging area, An optical sensor device that captures an image including a POS region which includes a set of POS subregions, including a POS subregion associated with a container configured to carry an object, a POS subregion associated with the scan window, and a POS subregion associated with the bagging area. It comprises a processing circuit configuration and memory, The memory includes instructions that can be executed by the processing circuit configuration, and thereby the processing circuit configuration, When the target object moves within the POS region, the optical sensor device acquires a set of sequential images representing the POS region. Based on a predetermined set of criteria associated with at least one of the set of POS subregions and an object movement track having a set of consecutive object positions of the target object as the target object is moved within the POS region, it is determined whether the target object was moved to the POS subregion associated with the bagging area without being scanned. POS system.

Images