Method and equipment for detecting remnants in elevator and storage medium

Abstract

The invention discloses a method and device for detecting remnants in an elevator and a storage medium. The method comprises the steps that original image data meeting background conditions are collected in a lift car of the elevator; drawing a detection area located at the bottom of the lift car and a light reference area located on the side wall of the lift car in the original image data; calculating a first brightness value of the detection area and a second brightness value of the light reference area in the original image data; querying one or more background image groups; screening background image data matched with the original image data in brightness from the background image group according to the first brightness value and the second brightness value of the original image data, and taking the background image data as target image data; and comparing the detection area of the target image data with the detection area of the original image data to identify whether there is a remnant in the car. According to the embodiment, the efficiency of detecting the remnants in the elevator is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevators, and in particular to a method, device and storage medium for detecting leftover objects in an elevator. Background Art

[0002] As residential buildings, shopping malls and other buildings become taller and taller, elevators have become one of the most commonly used vertical transportation tools. When taking an elevator, people occasionally leave their belongings in the car due to negligence or other reasons. If they are not cleaned in time, it will affect people's elevator experience.

[0003] Currently, image data is collected inside the elevator car, and the classification network in deep learning is used to detect the type of debris in the image data.

[0004] However, people may leave a wide variety of items in the elevator car, which places high demands on the generalization of the classification network and a high reliance on high-performance processing, increasing hardware costs and energy consumption. In addition, the lighting in the elevator car changes frequently, and false detection or missed detection is prone to occur when the elevator carpet is replaced, the floor is mopped, or objects are blocked (such as being wrapped or placed in a corner), resulting in low efficiency in detecting left-behind objects in the elevator. Summary of the Invention

[0005] In view of this, the present invention provides a method, device and storage medium for detecting objects left behind in an elevator, so as to improve the efficiency of detecting objects left behind in an elevator.

[0006] A first aspect of the present invention provides a method for detecting leftover objects in an elevator, comprising:

[0007] Collecting raw image data that meets background conditions in the elevator car; the background conditions are that there are no passengers in the car and the car door is closed;

[0008] Drawing a detection area located at the bottom of the car and a light reference area located at the side wall of the car in the original image data;

[0009] Calculating a first brightness value of a detection area and a second brightness value of a light reference area in the original image data;

[0010] Query one or more background image groups; the multiple frames of background image data in the background image group are all original image data collected in the elevator car in the past and meet the background conditions;

[0011] selecting background image data that matches the original image data in brightness from the background image group according to the first brightness value and the second brightness value of the original image data, and selecting the background image data as the target image data;

[0012] The detection area of ​​the target image data is compared with the detection area of ​​the original image data to identify whether there is any leftover object in the car.

[0013] A second aspect of the present invention provides a device for detecting objects left behind in an elevator, comprising:

[0014] An image acquisition module is used to acquire raw image data in the elevator car that meets background conditions; the background conditions are that there are no passengers in the car and the car door is closed;

[0015] an area recognition module, configured to draw a detection area at the bottom of the car and a light reference area at the side wall of the car in the original image data;

[0016] a brightness statistics module, configured to calculate a first brightness value of a detection area and a second brightness value of a light reference area in the original image data;

[0017] A background image group query module is used to query one or more background image groups; the multiple frames of background image data in the background image group are all original image data collected in the elevator car in the past and meet the background conditions;

[0018] a target image data screening module, configured to screen background image data that matches the original image data in brightness from the background image group according to the first brightness value and the second brightness value of the original image data, as target image data;

[0019] The detection area comparison module is used to compare the detection area of ​​the target image data with the detection area of ​​the original image data to identify whether there is any leftover object in the car.

[0020] A third aspect of the present invention provides an electronic device, comprising:

[0021] at least one processor; and

[0022] a memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for detecting left objects in an elevator as described in the first aspect above.

[0024] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for detecting objects left behind in an elevator as described in the first aspect above.

[0025] A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method for detecting left objects in an elevator as described in the first aspect above.

[0026] In this embodiment, original image data that meets the background conditions is collected in the elevator car; the background conditions are that there are no passengers in the car and the car door is closed; a detection area located at the bottom of the car and a light reference area located on the side wall of the car are drawn in the original image data; a first brightness value of the detection area and a second brightness value of the light reference area are calculated in the original image data; one or more background image groups are queried; the multiple frames of background image data in the background image group are all original image data that have been collected in the elevator car in the past and meet the background conditions; background image data that matches the original image data in brightness is screened in the background image group based on the first brightness value and the second brightness value of the original image data as target image data; the detection area of ​​the target image data is compared with the detection area of ​​the original image data to identify whether there are any remains in the car. This embodiment searches for background image data with similar recent brightness distribution based on the linear relationship in brightness between the bottom and side walls of the car, and detects debris on the current image data. The background image data is constantly changing, and can effectively adapt to situations such as replacing carpets, mopping the floor, and obstruction of objects in the car. The brightness intelligently adapts to changes in the car, which can improve the accuracy of detecting debris in the car and reduce false detections or missed detections. The operation of detecting debris mainly compares images and does not rely on classification networks, which can reduce dependence on high-performance processing, reduce hardware costs and energy consumption, and thus effectively improve the efficiency of detecting debris in elevators.

[0027] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 This is a flow chart of a method for detecting leftover objects in an elevator provided in Example 1 of the present invention.

[0030] Figure 2 This is a system architecture diagram for detecting objects left in a car, provided in accordance with a first embodiment of the present invention.

[0031] Figure 3 This is an example diagram of original image data that does not meet the background condition provided by the first embodiment of the present invention.

[0032] Figure 4 This is an example diagram of original image data that meets background conditions provided by the first embodiment of the present invention.

[0033] Figure 5 This is an example diagram of a fitting detection area provided in the first embodiment of the present invention.

[0034] Figure 6 This is an example diagram of brightness statistics of a detection area and a light reference area provided in the first embodiment of the present invention.

[0035] Figure 7 This is a flow chart of a method for detecting leftover objects in an elevator provided in the second embodiment of the present invention.

[0036] Figure 8 This is an example diagram of a brightness change function provided by the second embodiment of the present invention.

[0037] Figure 9 It is a structural diagram of a device for detecting leftover objects in an elevator provided in Example 3 of the present invention.

[0038] Figure 10 This is a structural diagram of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0041] Example 1

[0042] See also Figure 1 , shows a flow chart of a method for detecting objects left behind in an elevator provided by a first embodiment of the present invention. The method can be executed by a device for detecting objects left behind in an elevator. The device for detecting objects left behind in an elevator can be implemented in the form of hardware and / or software. The device for detecting objects left behind in an elevator can be configured in an electronic device. Figure 1 As shown, the method includes:

[0043] Step 101: Collect original image data that meets background conditions in an elevator car.

[0044] In different types of buildings, there are different transportation needs for people, pets, goods, etc. Therefore, different types of elevators can be deployed in buildings according to different transportation needs, such as passenger elevators, freight elevators, sightseeing elevators, etc.

[0045] Generally speaking, an elevator is a comprehensive system. Different types of elevators contain different components, and different maintenance strategies can be configured for different components.

[0046] In one example, the components of a certain type of elevator configuration include: an elevator control system, external call devices distributed on each floor, a car (including car doors), a motor for dragging the car (also known as a traction machine), a control cabinet, a speed limiter, a door opener, a car frame, a car door, a counterweight guide rail, a car guide rail, a guide rail support, an accompanying cable, a counterweight device, a compensation chain (cable), a floor door, a guide device for the compensation chain (cable), a buffer, and the like.

[0047] In some types of elevators, components such as the traction machine, control cabinet, speed limiter, and accompanying cable can be omitted.

[0048] These components can be divided into different sets according to their functions, thus forming various subsystems that support elevator operation. The elevator control system is connected to multiple systems of the elevator through serial ports or serial clock lines (SCL). The elevator control system monitors and controls the operation of each subsystem, allowing the car to move in the shaft and reach each floor of the building.

[0049] In one example, the controller includes a door system, a frequency conversion system, a calling system and a traction system, wherein the door system is used to control the doors of the elevator, the elevator doors include car doors and hall doors of elevator waiting halls on each floor, the type of the car doors is the same as the type of the hall doors, and they are switched at the same time (open or closed), the frequency conversion system is used to control the frequency converter, the calling system is used to control the logic of internal calls (calling the elevator in the car) and external calls (calling the elevator in the elevator waiting hall), and the traction system is used to control the movement of the car in the vertical direction (vertically upward or vertically downward) in the shaft.

[0050] like Figure 2 As shown, the manager or owner of the elevator can choose whether to install an edge terminal on the elevator based on factors such as the load status of the elevator. If they choose not to install an edge terminal, the controller maintains the original control logic and does not affect the normal operation of the elevator. If they choose to install an edge terminal, they can select a suitable computing device as the edge terminal of the elevator according to their needs. The edge terminal is combined with the original elevator control system to form a new elevator control system and redefine the operation logic of the elevator.

[0051] Generally speaking, edge terminals are computing devices with strong computing capabilities, such as computers, servers, or embedded devices. In addition, depending on different intelligent services, edge terminals can be equipped with graphics processing units (GPUs) or embedded neural network processors (NPUs).

[0052] An edge terminal refers to a new service platform built at the edge of the network near elevators, providing storage, computing, and networking resources. This platform allows some critical business applications to be deployed at the edge of the access network, reducing bandwidth and latency losses caused by network transmission and multi-level forwarding. Edge terminals are located between users and the cloud (server), closer to users (data sources) than traditional cloud platforms. They are miniaturized, distributed, and user-friendly. Massive amounts of data (such as audio data) no longer need to be uploaded to the cloud for processing, enabling data processing at the network edge. This reduces request response times and network bandwidth while ensuring data security and privacy.

[0053] In addition, the edge terminal can implement algorithm functions and model reasoning, communicate with the original elevator control system, and provide the original elevator control system with artificial intelligence (AI) and complex computing capabilities; the edge terminal can also communicate with the cloud to implement algorithm functions and model updates, and transfer function calls of the original elevator control system.

[0054] In actual applications, a camera can be installed at the top of the elevator car. The camera's shooting range covers the bottom and door of the car. The camera collects original image data from the inside of the car, forms video stream data, and transmits the video stream data to the edge terminal. The edge terminal transmits the video stream data to the server through a mobile network, etc., and the operation of detecting relics is performed in the edge terminal or server. At this time, machine learning technology or deep learning technology can be used to filter out the original image data that meets the background conditions from all the original image data.

[0055] The background condition is that there is no passenger in the car and the car door is closed. At this time, if there is an object in the car, it may be something left by the passenger.

[0056] For example, a target detection network can be set up for the car, and sample image data can be collected inside the car. Labels are annotated on the sample image data (such as people, the status of the car door (including open, closed), etc.), and the target detection network is supervised trained using the labeled sample image data.

[0057] Among them, the structure of the target detection network is not limited to artificially designed neural networks, such as YOLO (You Only Look Once), R-CNN (Region Convolutional Neural Networks) series, FPN (Feature Pyramid Networks), etc., and can also be a neural network optimized by a model quantization method, a neural network that searches for the characteristics of passengers and doors in the car through the NAS (Neural Architecture Search) method, etc. This embodiment does not impose any restrictions on this.

[0058] Furthermore, the target detection network can be a pre-trained neural network. On this basis, a small amount of labeled sample image data is used to fine-tune the target detection network, thereby reducing the cost of labeling samples and training the target detection network while ensuring the accuracy of target detection.

[0059] In this example, the original image data can be input into the target detection network to detect whether there is a person (i.e., a passenger) in the car and the status of the car door.

[0060] like Figure 3 As shown, if it is detected in the original image data that there is a person in the car and the state of the car door is open, it can be determined that the original image data does not meet the background condition.

[0061] like Figure 4 As shown, if there is no person in the car and the state of the car door is closed, it can be determined that the original image data meets the background condition.

[0062] Step 102: Draw the detection area at the bottom of the car and the light reference area at the side wall of the car in the original image data.

[0063] In this embodiment, a detection area M located at the bottom of the car and a light reference area N located on the side wall of the car can be drawn in the original image data. When a passenger drops an object, it usually falls to the bottom of the car. Therefore, the passenger's remains can usually be detected in the detection area M.

[0064] The light reference area N is a non-transparent area and belongs to the background area of ​​the car. It is used to reflect the ambient light intensity in the car. Therefore, its height is usually higher than a certain threshold (such as 2 meters) so that passengers will not block the light reference area N; the area of ​​the light reference area N is smaller than the area of ​​the detection area M; the number of light reference areas N is at least one. When there are multiple light reference areas N, multiple light reference areas N are evenly sampled on the side walls of the car to improve the stability of the brightness in the light reference area N.

[0065] In a specific implementation, a detection area can be fitted to the bottom of the car in the original image data. For ease of processing, the detection area M is kept rectangular or approximately rectangular. Depending on the installation angle of the camera, there is a certain degree of distortion at the bottom of the car in the original image data, such as Figure 5 As shown, if the detection area M is not a rectangle, the original image data can be rotated and other operations can be performed to make the detection area M approximately rectangular.

[0066] A first ratio is taken for the area of ​​the detection region to obtain a first threshold, and a second ratio is taken for the area of ​​the detection region to obtain a second threshold.

[0067] The value ranges of the first ratio and the second ratio are both (0, 1), and the first ratio is greater than the second ratio.

[0068] In the original image data, a light reference region located on the side wall of the car and having an area between a first threshold and a second threshold is determined.

[0069] For example, let the first ratio be 1 / 4 and the second ratio be 1 / 12, then S M / 4>S N >S M / 12, where S M is the area of ​​the detection area, S N is the area of ​​the light reference region.

[0070] like Figure 6 As shown in the figure, for a specific car, under the conditions that the camera installation position, FOV (Field of View), focal length and other factors are fixed, the situation inside the car is relatively fixed in the original image data. Then, the bottom and side walls of the car are also relatively fixed. Therefore, the detection area M and the light reference area (including N1, N2 and N3) can be pre-drawn offline, and the detection area and the light reference area are loaded into the original image data when detecting relics in real time.

[0071] Step 103: Calculate a first brightness value of the detection area and a second brightness value of the light reference area in the original image data.

[0072] In the original image data, the overall brightness value of the detection area is calculated and recorded as the first brightness value. Generally, the average brightness value of each pixel in the detection area is calculated as the first brightness value Lm[x], where x is the serial number of the original image data.

[0073] In the original image data, the overall brightness value of the light reference area is calculated and recorded as the second brightness value. Generally, the average brightness value of each pixel in the light reference area is calculated as the second brightness value Ln[x], where x is the serial number of the original image data.

[0074] When there are multiple light reference areas in the original image data, the second brightness values ​​of the multiple light reference areas may be averaged to serve as the final second brightness value.

[0075] For example, Figure 6 As shown, the first brightness value of the detection area M is 115.4, the second brightness value of the light reference area N1 is 207.0, the second brightness value of the light reference area N2 is 90.1, and the second brightness value of the light reference area N1 is 41.6.

[0076] In the environment inside the car, the ambient light is consistent. Therefore, Lm[x] and Ln[x] are positively correlated with the light changes inside the car, and there is a certain linear relationship between Lm[x] and Ln[x].

[0077] Step 104: Query one or more background image groups.

[0078] In this embodiment, one or more background image groups can be maintained for the car, and the original image data can be used to continuously update the one or more background image groups to adapt to environmental changes inside the car, such as posting advertisements, changing carpets, etc.

[0079] There are multiple background image groups with a linear relationship in the same background image group, and the multiple frames of background image data in the background image group are all original image data collected in the elevator car in the past and meet the background conditions.

[0080] Step 105 : Filter background image data that matches the original image data in brightness from the background image group according to the first brightness value and the second brightness value of the original image data, and use the background image data as the target image data.

[0081] Since the background image data is original image data from a recent period of history, the background image data includes a detection area and its first brightness value, and a light reference area and its second brightness value.

[0082] In this embodiment, the first brightness value and the second brightness value of the original image data and the first brightness value and the second brightness value of the background image data in the background image group can be compared with each other, and the background image data that matches the original image data in brightness can be screened as the target image data, that is, the brightness distribution of the target image data is the same as or similar to the brightness distribution of the original image data.

[0083] In one embodiment of the present invention, step 105 may include the following steps:

[0084] Step 1051 : Assign priorities to the background image group according to the first brightness value and the second brightness value of the original image data.

[0085] If there is a background image group, the priority of the background image group may be set to the highest by default.

[0086] If there are multiple (i.e., at least two) background image groups, the degree of similarity between the first brightness value and the second brightness value of the original image data and the first brightness value and the second brightness value of the background image group as a whole can be evaluated, so as to configure the priority of the background image group according to the degree of similarity, and filter the target image data that matches the original image data in brightness according to the priority, and use the target image data as a reference to detect relics in the original image data, thereby improving the efficiency of detecting relics.

[0087] In the initial state (such as the first detection of remains after car maintenance), query the brightness change function configured for the background image group; wherein the brightness change function represents the linear relationship between the first brightness value and the second brightness value in multiple frames of background image data in the same background image group.

[0088] Substitute the second brightness value in the original image data into the brightness change function to fit the target expected brightness value.

[0089] A difference between a first brightness value in the target image data and a target expected brightness value is calculated as a target brightness error, where the target expected brightness value is a brightness value expected for the detection area in the target image data under the constraint of the brightness value of the light reference area in the target image data.

[0090] Exemplarily, the brightness change function may be expressed as: Lm=a×Ln+b, where Lm is the first brightness value, Ln is the second brightness value, a is the slope, and b is the offset.

[0091] In this example, assume that the first brightness value of the original image data is Lm[x] and the second brightness value is Ln[x], and the second brightness value Ln[x] is substituted into the brightness change function to calculate the target expected brightness value Lm[x]′=a×Ln[x]+b. At this time, the target brightness error c=|Lm[x]-Lm[x]′|.

[0092] Priority is assigned to the background image group according to the target brightness error. The priority is negatively correlated with the target brightness error, i.e., the larger the target brightness error, the lower the priority, and vice versa, the smaller the target brightness error, the higher the priority.

[0093] For example, there are three background image groups T1, T2 and T3. The target brightness error of background image group T1 is c1, the target brightness error of background image group T2 is c2, and the target brightness error of background image group T3 is c3. If c1<c2<c3, then in terms of priority, T1>T2>T3.

[0094] Generally speaking, the original image data collected by the camera is relatively stable in a short period of time and will not mutate. Therefore, in a non-initial state, the priority of the background image group when detecting relics for the previous frame of original image data (that is, the priority of the background image group when detecting relics for the last time) can be queried as the priority of the background image group when detecting relics for the current frame of original image data. In this way, operations such as calculating the target brightness error are omitted, which can reduce the amount of calculation and save resources.

[0095] Step 1052: Read the background image group according to the priority as the target image group.

[0096] Step 1053: In the target image group, calculate the difference between the second brightness value of the original image data and the second brightness value of the background image data as the sidewall brightness error.

[0097] Step 1054: In the target image group, determine that the background image data with the smallest sidewall brightness error matches the original image data in terms of brightness, and use it as the target image data.

[0098] In this embodiment, the background image groups may be read in sequence according to priority and recorded as target image groups.

[0099] In the current target image group, the difference between the second brightness value of the original image data and the second brightness value of each background image data is calculated and recorded as a brightness error.

[0100] Assuming that the second brightness value of the original image data is Ln[x], and the second brightness value of each background image data in the target image group is Ln[y], then the brightness error ΔLn=|Ln[x]-Ln[y]|.

[0101] In the target image group, the brightness errors of each side wall are compared, and the background image data with the smallest side wall brightness error is screened out, that is, the background image data with the second brightness value closest to the original image data is screened out. The background image data matches the original image data in brightness and is used as the target image data.

[0102] Step 106: Compare the detection area of ​​the target image data with the detection area of ​​the original image data to identify whether there is any leftover object in the car.

[0103] In this embodiment, the detection area of ​​the target image data can be compared with the detection area of ​​the original image data, and the distribution information of the similarities and differences between the detection area of ​​the target image data and the detection area of ​​the original image data can be used to identify whether there are any remaining objects in the car.

[0104] like Figure 2 As shown, when it is detected that there are objects left in the car, a prompt message can be generated, pushed to the client, and the monitoring personnel can be notified. The monitoring personnel can retrieve the video stream data in the car from the server for manual review. When the manual review shows that there are objects left, subsequent processing such as cleaning up the objects, registering lost objects, and tracking the owner can be carried out.

[0105] In a specific implementation, the original image data and the target image data are grayscaled respectively, the detection area in the original image data is converted into a first grayscale area, and the detection area in the target image data is converted into a second grayscale area.

[0106] Among them, in order to improve the efficiency of detecting leftovers in the elevator car in real time, the target image data can be grayscaled offline, and when detecting leftovers in the elevator car in real time, the second grayscale area can be directly read.

[0107] The first similarity R between the first grayscale region and the second grayscale region is calculated using methods such as square difference, normalized cross-correlation, and normalized correlation coefficient.

[0108] Illustratively, the value range of the first similarity R in a certain manner is [-1, 1]. A larger value indicates a higher matching degree between the first grayscale region and the second grayscale region.

[0109] If the first similarity R is greater than or equal to a preset first threshold Y0 (ie, R≥Y0), it is determined that there is no leftover object in the car, and the priority of the background image group corresponding to the target image data is adjusted to the highest level.

[0110] When there are at least three background image groups, the background image groups other than the background image group corresponding to the target image data have a lower impact on the detection of remains, and the priorities of the other background image groups can be adjusted in any way. For example, the priority of the background image group with the highest original priority level can be set to the original priority of the background image group corresponding to the target image data, and so on. This embodiment does not impose any restrictions on this.

[0111] If the first similarity R is less than a preset first threshold Y0 (ie, R<Y0), the process switches to the target image data in the target image group of the next priority level; and the process returns to grayscale processing of the original image data and the target image data.

[0112] If the first similarity R of all target image data is less than a preset first threshold Y0 (ie, R<Y0), the detection area in the original image data is divided into a plurality of first sub-areas according to a specified segmentation method (such as a nine-square grid), wherein each first sub-area has a number.

[0113] For the target image data in the background image group with the highest priority, the detection area in the target image data is divided into a plurality of second sub-areas at different positions according to a specified segmentation method (such as a nine-square grid), wherein each second sub-area has a number.

[0114] For the same number, the second similarity Ri between the first sub-region and the second sub-region is calculated using methods such as square difference, normalized cross-correlation, and normalized correlation coefficient, where i is the sequence number of the position.

[0115] In multiple consecutive frames of original image data, if the second similarity Ri corresponding to the number is less than the preset second threshold Y1 (i.e., Ri<Y1), the number is marked with a change bit, and there may be a remnant, that is, the state of the change bit remains unchanged for a certain period of time (such as 3 seconds).

[0116] If the number of changed bits is less than the preset third threshold, it means that there are changes in a small range at the bottom of the car in a short period of time, which meets the characteristics of leftovers, and it is determined that there are leftovers in the car.

[0117] If the number of changed bits is greater than or equal to the preset third threshold, it means that a large area of ​​the bottom of the car has changed in a short period of time, which meets the characteristics of changing the background. Then it is determined that the background of the bottom of the car has been switched, for example, the carpet at the bottom of the car has been replaced, advertisements have been posted at the bottom of the car, etc.

[0118] The third threshold is related to the number of positions, that is, the third threshold tends to the number of positions and is smaller than the number of positions. For example, when the number of positions is 9, the third threshold is 7.

[0119] In this embodiment, original image data that meets the background conditions is collected in the elevator car; the background conditions are that there are no passengers in the car and the car door is closed; a detection area located at the bottom of the car and a light reference area located on the side wall of the car are drawn in the original image data; a first brightness value of the detection area and a second brightness value of the light reference area are calculated in the original image data; one or more background image groups are queried; the multiple frames of background image data in the background image group are all original image data that have been collected in the elevator car in the past and meet the background conditions; background image data that matches the original image data in brightness is screened in the background image group based on the first brightness value and the second brightness value of the original image data as target image data; the detection area of ​​the target image data is compared with the detection area of ​​the original image data to identify whether there are any remains in the car. This embodiment searches for background image data with similar recent brightness distribution based on the linear relationship in brightness between the bottom and side walls of the car, and detects debris on the current image data. The background image data is constantly changing, and can effectively adapt to situations such as replacing carpets, mopping the floor, and obstruction of objects in the car. The brightness intelligently adapts to changes in the car, which can improve the accuracy of detecting debris in the car and reduce false detections or missed detections. The operation of detecting debris mainly compares images and does not rely on classification networks, which can reduce dependence on high-performance processing, reduce hardware costs and energy consumption, and thus effectively improve the efficiency of detecting debris in elevators.

[0120] Example 2

[0121] See also Figure 7 , shows a flow chart of a method for detecting objects left behind in an elevator provided by the second embodiment of the present invention. This embodiment adds an operation of updating background image data based on the previous embodiment. Figure 7 As shown, the method includes:

[0122] Step 701: Collect original image data that meets background conditions in an elevator car.

[0123] The background condition is that there is no passenger in the elevator car and the door of the elevator car is closed.

[0124] Step 702: Draw the detection area at the bottom of the car and the light reference area at the side wall of the car in the original image data.

[0125] Step 703: Calculate a first brightness value of the detection area and a second brightness value of the light reference area in the original image data.

[0126] Step 704: Mark the original image data as background image data at every preset time period.

[0127] In this embodiment, the original image data can be collected from the video stream data generated by the camera at a certain time interval (such as 30 minutes) and updated as background image data. That is, at every preset time period (such as 30 minutes), the original image data is marked as background image data, the time when the original image data is generated is recorded, and it is stored in the form of a cache queue, database, etc.

[0128] Step 705: If the amount of background image data reaches a preset upper limit, a linear brightness change function is fitted according to the first brightness value and the second brightness value in the background image data.

[0129] In this embodiment, the number of current background image data can be counted. If the number of background image data reaches a preset upper limit (such as 30), the background image data can be re-divided into background image groups, and a linear brightness change function can be fitted based on the first brightness value and the second brightness value in the background image data in the same background image group.

[0130] In a specific implementation, a brightness point is constructed with the first brightness value in the background image data as the y coordinate and the second brightness value in the background image data as the x coordinate.

[0131] Substitute the second brightness value in the background image data into the linear brightness change function to calculate the sample expected brightness value, wherein the sample expected brightness value is the expected brightness value of the detection area in the background image data under the constraint of the brightness value of the light reference area in the background image data.

[0132] On the basis of the sample expected brightness value, the preset standard brightness error is added and subtracted respectively to obtain the error range. That is, the sample expected brightness value is the range of reasonable fluctuation of the brightness value of the detection area in the background image data.

[0133] Since the environment inside the car is relatively simple, an upper limit (such as 3) can be set for the number of background image groups. Under the condition that the first brightness value in the background image data is within the error range, the brightness points are divided into one or more clusters. That is, considering that there is an upper limit on the number of clusters, with the constraint condition that the first brightness value in the background image data is within the error range and the optimization goal is to minimize the number of background image groups, the brightness points are clustered to obtain one or more clusters, each of which has multiple brightness points.

[0134] The parameters (such as slope, offset, etc.) in the brightness change function are fitted using the brightness points in the same cluster. Then, in the same cluster, the background image data to which the brightness points belong are divided into the same background image group.

[0135] Exemplarily, the brightness change function can be expressed as: Lm=a×Ln+b, where Lm is the first brightness value, Ln is the second brightness value, a is the slope to be fitted, and b is the offset to be fitted.

[0136] In this example, assume that the first brightness value of the original image data is Lm[x], the second brightness value is Ln[x], and the marked brightness error is c. The marked brightness error is the maximum error introduced when fitting the brightness change function, which is usually an empirical value. Then, the first brightness value in the background image data is within the error range and can be expressed as: a×Ln[x]+bc<Lm[x]<a×Ln[x]+b+c.

[0137] like Figure 8 As shown, considering the situations such as replacing the carpet at the bottom of the car and turning off the lighting in the car when there are no passengers, this example uses three background image groups to fit three brightness change functions, that is, fits three groups of parameters to the brightness change function.

[0138] Of course, if one or two background image groups are sufficient to cover all brightness points, the remaining background image groups may be set to null values, that is, no brightness points are included in the background image groups.

[0139] Among them, the x-axis of the coordinate axis is the second brightness value Ln, and the y-axis is the first brightness value Lm. At different brightness points, the second brightness value Ln may be the same, while the first brightness value Lm may be different. For example, in the background image group cluster1, a brightness point Ln is 2 and Lm is approximately 8, while in the background image group cluster1, a brightness point Ln is 2 and Lm is approximately 20.

[0140] The brightness change function fitted using background image group cluster2 reflects a general linear relationship. Both the light reference area N and the detection area M increase with the brightness of the ambient light. The brightness change function fitted using background image group cluster1 can be understood as a darker carpet laid at the bottom of the car. The light reference area N increases with the brightness of the ambient light, while the detection area M does not change significantly with the brightness of the ambient light. The brightness change function fitted using background image group cluster2 is a reserved supplement and correction for common scenarios.

[0141] Step 706: Remove the oldest background image data.

[0142] In this embodiment, the background image data with the earliest generation time may be removed from a cache queue, a database, or the like.

[0143] The amount of background image data to be removed is related to the upper limit of the amount of background image data. For example, if the upper limit of the amount of background image data is 30, the 10 frames of background image data with the earliest generation time are removed.

[0144] Example 3

[0145] See also Figure 9 , shows a schematic diagram of the structure of a device for detecting leftovers in an elevator provided by the third embodiment of the present invention. Figure 9 As shown, the device includes:

[0146] The image acquisition module 901 is used to collect raw image data in the elevator car that meets the background condition; the background condition is that there are no passengers in the car and the car door is closed;

[0147] an area recognition module 902 for drawing a detection area at the bottom of the car and a light reference area at the side wall of the car in the original image data;

[0148] A brightness statistics module 903 is configured to calculate a first brightness value of a detection area and a second brightness value of a light reference area in the original image data;

[0149] Background image group query module 904 is used to query one or more background image groups; the multiple frames of background image data in the background image group are all original image data collected in the elevator car in the past and meet the background conditions;

[0150] A target image data screening module 905 is configured to screen background image data that matches the original image data in brightness from the background image group according to the first brightness value and the second brightness value of the original image data, as target image data;

[0151] The detection area comparison module 906 is configured to compare the detection area of ​​the target image data with the detection area of ​​the original image data to identify whether there is any object left in the car.

[0152] In one embodiment of the present invention, the region identification module 902 includes:

[0153] A detection area fitting module, configured to fit a detection area to the bottom of the car in the original image data;

[0154] A first threshold generating module, configured to obtain a first threshold by taking a first ratio of the area of ​​the detection region;

[0155] A second threshold generating module, configured to obtain a second threshold by taking a second ratio of the area of ​​the detection region; wherein the first ratio is greater than the second ratio;

[0156] The light reference area determination module is used to determine, in the original image data, a light reference area located on the side wall of the car and having an area between the first threshold and the second threshold.

[0157] In one embodiment of the present invention, the target image data screening module 905 includes:

[0158] a priority configuration module, configured to configure the priority of the background image group according to the first brightness value and the second brightness value of the original image data;

[0159] a target image group reading module, configured to read the background image group according to the priority as the target image group;

[0160] a sidewall brightness error calculation module, configured to calculate, in the target image group, a difference between a second brightness value of the original image data and a second brightness value of the background image data as a sidewall brightness error;

[0161] The sidewall brightness error screening module is used to determine, in the target image group, the background image data with the smallest sidewall brightness error that matches the original image data in terms of brightness, as the target image data.

[0162] In one embodiment of the present invention, the priority configuration module includes:

[0163] A brightness change function query module, configured to query a brightness change function configured for the background image group; the brightness change function represents a linear relationship between a first brightness value and a second brightness value in multiple frames of the background image data;

[0164] a target expected brightness value fitting module, configured to substitute the second brightness value in the original image data into the brightness change function to calculate the target expected brightness value;

[0165] a target brightness error calculation module, configured to calculate a difference between a first brightness value in the original image data and the target expected brightness value as a target brightness error;

[0166] A target brightness error configuration module is used to configure the priority of the background image group according to the target brightness error; the priority is negatively correlated with the target brightness error.

[0167] In one embodiment of the present invention, the priority configuration module further includes:

[0168] The priority query module is used to query the priority of the background image group when detecting remains on the original image data of the previous frame, as the priority of the background image group when detecting remains on the original image data of the current frame.

[0169] In one embodiment of the present invention, the detection area comparison module 906 includes:

[0170] A first grayscale area conversion module, configured to convert the detection area in the original image data into a first grayscale area;

[0171] A second grayscale area conversion module, configured to convert the detection area in the target image data into a second grayscale area;

[0172] A first similarity calculation module, configured to calculate a first similarity between the first grayscale region and the second grayscale region;

[0173] a first left-behind determining module, configured to determine that no left-behind objects are present in the car if the first similarity is greater than or equal to a preset first threshold, and adjust the priority of the background image group corresponding to the target image data to the highest level;

[0174] a target image group switching module, configured to switch to the target image data in the background image group of the next priority if the first similarity is less than a preset first threshold; and return to execute the second grayscale area conversion module;

[0175] a first sub-region division module, configured to divide the detection region in the original image data into a plurality of first sub-regions if the first similarities of all the target image data are less than a preset first threshold; each of the first sub-regions is numbered;

[0176] A second sub-region division module is configured to divide the detection region in the target image data in the background image group with the highest priority into a plurality of second sub-regions; each of the second sub-regions has a number;

[0177] a second similarity calculation module, configured to calculate a second similarity between the first sub-region and the second sub-region for the same serial number;

[0178] a change bit marking module, configured to mark a change bit on a number in a plurality of consecutive frames of the original image data if the second similarities corresponding to the number are all less than a preset second threshold;

[0179] a second left-behind determining module, configured to determine that there is left-behind in the car if the number of the changed bits is less than a preset third threshold;

[0180] The background switching determination module is configured to determine that the car bottom background is switched if the number of the changed bits is greater than or equal to a preset third threshold.

[0181] In one embodiment of the present invention, it further comprises:

[0182] A background image data marking module, configured to mark the original image data as background image data at intervals of a preset time period;

[0183] a brightness change function fitting module, configured to fit a linear brightness change function according to the first brightness value and the second brightness value in the background image data if the amount of the background image data reaches a preset upper limit;

[0184] The background image data removal module is used to remove the oldest part of the background image data.

[0185] In one embodiment of the present invention, the brightness change function fitting module includes:

[0186] A brightness point construction module, configured to construct a brightness point using the first brightness value in the background image data as a y coordinate and the second brightness value in the background image data as an x ​​coordinate;

[0187] a sample expected brightness value fitting module, configured to substitute the second brightness value in the background image data into a linear brightness variation function to fit the sample expected brightness value;

[0188] An error range generating module is configured to add a preset standard brightness error and subtract a preset standard brightness error from the expected brightness value of the sample to obtain an error range;

[0189] a clustering module, configured to divide the brightness points into one or more clusters under the condition that the first brightness value in the background image data is within the error range;

[0190] a parameter fitting module, configured to fit the parameters of the brightness variation function using the brightness points in the same cluster;

[0191] The background image group division module is used to divide the background image data to which the brightness points belong into the same background image group in the cluster.

[0192] The device for detecting objects left behind in an elevator provided by an embodiment of the present invention can execute the method for detecting objects left behind in an elevator provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to executing the method for detecting objects left behind in an elevator.

[0193] Example 4

[0194] See also Figure 10 , which shows a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0195] like Figure 10 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0196] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0197] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for detecting objects left behind in an elevator.

[0198] In some embodiments, the method for detecting a leftover object in an elevator can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for detecting a leftover object in an elevator described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for detecting a leftover object in an elevator by any other appropriate means (e.g., by means of firmware).

[0199] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0200] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0201] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0202] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0203] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0204] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0205] Example 5

[0206] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method for detecting left objects in an elevator as provided in any embodiment of the present invention.

[0207] The computer program product may be implemented by writing computer program code for performing the operations of the present invention in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0208] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0209] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for detecting leftover objects in an elevator, characterized in that: include: Collecting original image data that meets background conditions in the elevator car; The background condition is that there is no passenger in the car and the car door is closed; Drawing a detection area located at the bottom of the car and a light reference area located at the side wall of the car in the original image data; Calculating a first brightness value of a detection area and a second brightness value of a light reference area in the original image data; Query one or more background image groups; the multiple frames of background image data in the background image group are all original image data collected in the elevator car in the past and meet the background conditions; selecting background image data that matches the original image data in brightness from the background image group according to the first brightness value and the second brightness value of the original image data, and selecting the background image data as the target image data; The detection area of ​​the target image data is compared with the detection area of ​​the original image data to identify whether there is any leftover object in the car.

2. The method according to claim 1, characterized in that Drawing the detection area at the bottom of the car and the light reference area at the side wall of the car in the original image data includes: fitting a detection area for the car bottom in the original image data; Taking a first ratio of the area of ​​the detection region to obtain a first threshold; Taking a second ratio of the area of ​​the detection region to obtain a second threshold; wherein the first ratio is greater than the second ratio; In the original image data, a light reference area located on the side wall of the car and having an area between the first threshold and the second threshold is determined.

3. The method according to claim 1, characterized in that The step of screening background image data that matches the original image data in brightness from the background image group according to the first brightness value and the second brightness value of the original image data as target image data includes: configuring a priority for the background image group according to a first brightness value and a second brightness value of the original image data; Read the background image group according to the priority as the target image group; In the target image group, calculating a difference between a second brightness value of the original image data and a second brightness value of the background image data as a sidewall brightness error; In the target image group, the background image data with the smallest sidewall brightness error is determined to match the original image data in terms of brightness and is used as the target image data.

4. The method according to claim 3, characterized in that The configuring the priority of the background image group according to the first brightness value and the second brightness value of the original image data includes: Querying a brightness change function configured for the background image group; the brightness change function represents a linear relationship between a first brightness value and a second brightness value in a plurality of frames of the background image data; Substituting the second brightness value in the original image data into the brightness change function to calculate the target expected brightness value; Calculating a difference between a first brightness value in the original image data and the target expected brightness value as a target brightness error; Priorities are configured in the background image group according to the target brightness error; the priorities are negatively correlated with the target brightness error.

5. The method according to claim 4, characterized in that The configuring priority of the background image group according to the first brightness value and the second brightness value of the original image data further includes: The priority of the background image group when detecting remains for the original image data of the previous frame is queried as the priority of the background image group when detecting remains for the original image data of the current frame.

6. The method according to claim 3, characterized in that The comparing the detection area of ​​the target image data with the detection area of ​​the original image data to identify whether there is any object left in the car includes: Converting the detection area in the original image data into a first grayscale area; Converting the detection area in the target image data into a second grayscale area; Calculating a first similarity between the first grayscale area and the second grayscale area; If the first similarity is greater than or equal to a preset first threshold, it is determined that there is no leftover object in the car, and the priority of the background image group corresponding to the target image data is adjusted to the highest level; If the first similarity is less than a preset first threshold, switching to the target image data in the target image group of the next priority; returning to executing the step of converting the detection area in the target image data into a second grayscale area; If the first similarities of all the target image data are less than a preset first threshold, dividing the detection area in the original image data into a plurality of first sub-areas; each of the first sub-areas has a number; For the target image data in the background image group with the highest priority, dividing the detection area in the target image data into a plurality of second sub-areas; each of the second sub-areas has a number; For the same number, calculating a second similarity between the first sub-region and the second sub-region; In a plurality of consecutive frames of the original image data, if the second similarities corresponding to the numbers are all less than a preset second threshold, marking the changed bits on the numbers; If the number of the changed bits is less than a preset third threshold, it is determined that there is something left in the car; If the number of the changed bits is greater than or equal to a preset third threshold, it is determined that the car bottom background is switched.

7. The method according to any one of claims 1 to 6, characterized in that Also includes: At every preset time period, marking the original image data as background image data; If the amount of the background image data reaches a preset upper limit, fitting a linear brightness change function according to the first brightness value and the second brightness value in the background image data; The background image data with the oldest time is removed.

8. The method according to claim 7, characterized in that The step of fitting a linear brightness change function based on the first brightness value and the second brightness value in the background image data includes: Constructing a brightness point using the first brightness value in the background image data as a y coordinate and the second brightness value in the background image data as an x ​​coordinate; Substituting the second brightness value in the background image data into a linear brightness change function to fit the sample expected brightness value; Based on the expected brightness value of the sample, a preset standard brightness error is added and a preset standard brightness error is subtracted to obtain an error range; Dividing the brightness points into one or more clusters under the condition that a first brightness value in the background image data is within the error range; Fitting the parameters of the brightness variation function using the brightness points in the same cluster; In the cluster, the background image data to which the brightness point belongs are divided into the same background image group.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for detecting left objects in an elevator according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for detecting leftover objects in an elevator according to any one of claims 1 to 8 is implemented.

Images