Object positioning method and apparatus, and computer system

Abstract

The present application discloses an object positioning method, and corresponding device and computer system, of which the method comprises: receiving a color image and a depth image to which the color image corresponds; fusing the color image with the depth image to obtain a target image, wherein the target image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and inputting the target image in a preset model for recognition, and positioning a position of a target object in the target image, wherein an input layer of the preset model includes RGB channels and an Alpha channel, whereby, relative to recognition performed merely on the basis of the color image, efficiency of and precision in positioning the target object are enhanced.

Description

OBJECT POSITIONING METHOD AND APPARATUS, AND COMPUTER SYSTEM BACKGROUND OF THE INVENTION Technical Field

[0001] The present invention relates to the field of image recognition, and more particularly to an object positioning method, and corresponding device and computer system. Description of Related Art

[0002] With the development of the internet technology, unmanned stores have gradually been in vogue in the field of novel retails. In the state of the art, anti-theft monitoring of commodities in unmanned stores mostly relies on the radio frequency identification (RFID) technology, whereby each commodity is required to be previously labeled with an anti-theft label, so the cost is high and use is inconvenient. Even if the face recognition technique is applied to recognize and confirm such behaviors of consumers as their going into and out of the stores, there is still a risk of violating the privacy of consumers due to the recognition of their faces. SUMMARY OF THE INVENTION

[0003] In order to deal with the deficiencies in prior-art technology, a main objective of the present invention it is to provide an object positioning method, so as to realize positioned detection of objects.

[0004] In order to achieve the above objective, according to the first aspect, the present invention provides an object positioning method that comprises:

[0005] receiving a color image and a depth image to which the color image corresponds;

[0006] fusing the color image with the depth image to obtain a target image, wherein the target image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and

[0007] inputting the target image in a preset model for recognition, and positioning a position of a target object in the target image, wherein an input layer of the preset model includes RGB channels and an Alpha channel.

[0008] In some embodiments, before fusing the color image with the depth image, the method further comprises:

[0009] performing an image normalization operation on the depth image according to a preset method and a preset parameter.

[0010] In some embodiments, before fusing the color image with the depth image, the method further comprises:

[0011] performing image registration on the normalized depth image and the color image.

[0012] In some embodiments, the color image is shot by a first camera, the depth image is shot by a second camera, and performing image registration on the color image and the depth image includes:

[0013] employing a checkerboard method to calibrate the first camera and the second camera, and obtaining corresponding transformation matrixes of the first camera and the second camera; and

[0014] performing image registration on the color image and the depth image according to the transformation matrixes.

[0015] In some embodiments, before inputting the target image in a preset model for recognition, the method further comprises:

[0016] performing data enhancement on the target image.

[0017] In some embodiments, a process of training the preset model includes:

[0018] obtaining a training image set, wherein the image set consists of a color image previously marked with a sample target and a depth image to which the color image corresponds;

[0019] performing an image normalization operation on the depth image, and converting the same to a preset format;

[0020] performing image registration on the color image and the corresponding depth image;

[0021] fusing the depth image with the corresponding color image to obtain a testing image, wherein the testing image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and

[0022] taking the testing image as input to a target model, correspondingly taking the previously marked sample target as expected output from the target model, and continually training the target model until the target model satisfies a preset condition.

[0023] In some embodiments, the target model is obtained by the following mode:

[0024] modifying an input layer of a Yolov3 model as four channels, and obtaining an improved Yolov3 model, wherein the input layer includes RGB channels and an Alpha channel; and

[0025] clipping a backbone network of the improved Yolov3 model according to a preset clipping parameter, and obtaining the target model.

[0026] According to the second aspect, the present application provides an object positioning device that comprises:

[0027] a receiving module, for receiving a color image and a depth image to which the color image corresponds;

[0028] an image processing module, for fusing the color image with the depth image to obtain a target image, wherein the target image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and

[0029] a matching module, for inputting the target image in a preset model for recognition, and positioning a position of a target object in the target image, wherein an input layer of the preset model includes RGB channels and an Alpha channel.

[0030] In some embodiments, the image processing module is further usable for performing image registration on the color image and the depth image.

[0031] According to the third aspect, the present application provides a computer system that comprises:

[0032] one or more processor(s); and

[0033] a memory, associated with the one or more processor(s), for storing a program instruction that performs the following operations when it is read and executed by the one or more processor(s):

[0034] receiving a color image and a depth image to which the color image corresponds;

[0035] fusing the color image with the depth image to obtain a target image, wherein the target image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and

[0036] inputting the target image in a preset model for recognition, and positioning a position of a target object in the target image.

[0037] The present invention achieves the following advantageous effects.

[0038] The present invention discloses receiving a color image and a depth image to which the color image corresponds, fusing the color image with the depth image to obtain a target image, wherein the target image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image, inputting the target image in a preset model for recognition, and positioning a position of a target object in the target image; by recognizing the image fused by the color image and the depth image, relative to recognition performed merely on the basis of the color image or the depth image, efficiency of and precision in positioning the target object in the target image are greatly enhanced, tracking of the shifting path of the target object can be realized according to the position of the positioned target object, purchasing paths of consumers can be tracked when applied to unmanned stores, application can also be found in the analysis of purchasing behaviors of consumers at the same time of guaranteeing safety of goods, and purchasing experiences of consumers are enhanced.

[0039] The present application further discloses performing such image processing operations as image normalization on the depth image and image registration on the color image and the depth image before fusing the color image and the depth image, whereby precision in positioning the target object is further enhanced.

[0040] The present application proposes inputting the target image in a preset model for recognition after performing data enhancement on the target image, whereby positioning efficiency is ensured.

[0041] Not all products of the present invention are necessarily required to simultaneously possess all the aforementioned effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly describe the technical solutions in the embodiments of the present invention, drawings required for the illustration of the embodiments will be briefly introduced below. Apparently, the drawings described below are merely directed to some embodiments of the present invention, and it is possible for persons ordinarily skilled in the art to base on these drawings to acquire other drawings without spending creative effort in the process.

[0043] Fig. 1 is a flowchart illustrating people detection in an unmanned store provided by the embodiments of the present application;

[0044] Fig. 2 is a view schematically illustrating the framework of a Yolov3-4channel network structure provided by an embodiment of the present application;

[0045] Fig. 3 is a flowchart illustrating the method provided by an embodiment of the present application;

[0046] Fig. 4 is a view illustrating the structure of the device provided by an embodiment of the present application; and

[0047] Fig. 5 is a view illustrating the structure of the computer system provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0048] In order to make more lucid and clear the objectives, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and comprehensively described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the embodiments as described are merely partial embodiments, rather than the entire embodiments, of the present invention. All other embodiments obtainable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without spending any creative effort shall all be covered by the protection scope of the present invention.

[0049] As noted in the Description of Related Art, in order to ensure safety of commodities in an unmanned store, it is possible to install cameras in the unmanned store, to analyze moving tracks of customers according to images shot by the cameras, to recognize suspicious customers according to the moving tracks, to also analyze purchasing behaviors of customers according to the moving tracks, and to enhance purchasing experiences of customers.

[0050] In order to achieve the above objectives, the present application discloses inputting a target image in a preset model, and determining the position of a target object according to the output result of the model, whereby is realized real-time recognition of customers' positions and moving tracks.

[0051] Embodiment 1

[0052] An example is taken by the use of a Yolov3 model to detect images shot in an unmanned store and to recognize the position of a customer, as shown in Fig. 1, the method can be realized through the following steps.

[0053] The Yolov3 model is a general target detection model usable for processing images, and extracting such target objects in the images as people and commodities, etc.

[0054] However, this model can only be used to detect 3-channel RGB color images, but cannot fuse depth images with color images, and also cannot detect RGBD images obtained after such fusing.

[0055] RGB stands for the common color standards in the field of industry, whereby various colors are obtained by changing the values of the three color channels as red, green and blue and through mutual superpositions amongst them, and the standards can almost subsume all colors perceptible to the human vision.

[0056] RGBD stands for the addition of an Alpha channel based on the 3-channel RGB, and adds additional information originating from a depth image to the RGB image. Pixel values of the depth image represent the actual distance between the camera and the shot object, and an RGBD image fused by the depth image and a color image can more clearly express the actual status of the shot object than a single color image, so the result obtained on the basis of recognition of an RGBD image is more precise than on the basis of recognition of the color image.

[0057] In order to enable the Yolov3 model to support recognition of the RGBD image, the model should be improved, and the improving process includes:

[0058] modifying the input layer of the Yolov3 model to change it from being able to be input only with a 3-channel RGB image to being able to be input with the RGBD image that includes RGB channels and an Alpha channel, and the model thusly modified can be renamed as a Yolov3-4channel network model.

[0059] In order to accelerate the reasoning speed of the model and to enhance the output efficiency of the model, the Yolov3-4channel Backbone network layer can be clipped according to a preset clipping parameter, to reduce the number of model layers of the model to accelerate computation.

[0060] Fig. 2 is a view schematically illustrating the framework of a Yolov3-4channel network structure that includes an input layer, a Res layer, a convolutional layer (conv), an upper sampling layer (upSample), a Yolo layer, and a concat layer.

[0061] In order to obtain the color image and the depth image, it is possible to install a color camera and a depth camera respectively in the unmanned store to collect color images and depth images, the installation height is 3 to 4 meters above ground, and the installation angle is perpendicular to the ground.

[0062] After image collection and model improvement have been completed, it can be started to train the model to obtain the preset model, and the specific training process includes the following steps.

[0063] Step A – collecting an image dataset.

[0064] The dataset contains color images and corresponding depth images, 85% of the dataset can be used for training the model, while the remaining 15% is used for testing the model.

[0065] Step B – marking people contained in the color image with a VOC format, and converting the color image from a BGR pattern to an RGB pattern.

[0066] BGR is a color standard that is inverse to the RGB sequence, and represents the sequence of blue, green, red.

[0067] VOC is an image marking rule usable for marking target objects in images.

[0068] Step C – preprocessing the depth image.

[0069] The preprocessing process can include:

[0070] performing an image normalization operation on the depth image;

[0071] the image normalization can include:

[0072] suppose significant bits of the depth image are 16 bit, the camera is distanced from the ground at a height of 4000 mm, then the following formula is used to normalize the depth image within the interval of [0, 255].

[0073] <semantics>ndepth=depth / 4000*255<annotation encoding="application / x-tex">ndepth = depth / 4000 * 255< / annotation>< / semantics>, depth represents the depth read from this depth image.

[0074] The normalized depth image is converted to a unit8 format, which is a data type of pictures.

[0075] Step D – performing image registration on the color image and the corresponding depth ımage.

[0076] The specific process of image registration includes:

[0077] with respect to a first camera that shots color images and a second camera that shots depth images, employing a checkerboard calibration method to calculate internal reference matrixes of the first camera and the second camera respectively, and to calculate external reference matrixes of the first camera and the second camera relative to a preset checkerboard, and calculating corresponding transformation matrixes of the first camera and the second camera according to the internal reference matrixes and the external reference matrixes; and

[0078] performing image registration on the color image and the corresponding depth image according to the transformation matrixes.

[0079] Step E – taking the depth image as an Alpha channel of a target image, and taking the color image as RGB channels of the target image to perform image fusion, and obtaining a 4-channel RGBD target image.

[0080] Step F – performing data enhancement on the target image.

[0081] The data enhancement method includes such image processing methods as image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting, etc.

[0082] Step G – taking the target image as input to the improved model, correspondingly taking marked people as expected output from the model, and training the model.

[0083] The training process includes: modifying the training parameter of the model, employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends can then be regarded that the model has completed training, and outputting the preset model of the target.

[0084] After the preset model of the target has been obtained, the preset model can then be used to recognize images, and the recognizing process includes:

[0085] Step A – receiving the color image and a depth image to which the color image corresponds;

[0086] Step B – performing an image normalization operation on the depth image according to a preset method and a preset parameter, and converting the same to a unit8 format;

[0087] Step C – performing image registration on the depth image obtained in step B and the color image;

[0088] the image registration process includes:

[0089] with respect to a first camera that shots color images and a second camera that shots depth images, employing a checkerboard calibration method to calculate internal reference matrixes of the first camera and the second camera respectively, and to calculate external reference matrixes of the first camera and the second camera relative to a preset checkerboard, and calculating corresponding transformation matrixes of the first camera and the second camera according to the internal reference matrixes and the external reference matrixes; and

[0090] performing image registration on the color image and the corresponding depth image according to the transformation matrixes.

[0091] Step D – fusing the color image with the depth image to generate a target image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image, and performing data enhancement on the target image;

[0092] the data enhancement includes, but is not limited to, such image processing methods as image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting, etc.

[0093] Step E – inputting the target image in the preset model for recognition, and positioning a position of the target object in the target image.

[0094] Through the above method it is made possible to recognize such a target object as people in the target image, precision and efficiency in people recognition are enhanced, and such subsequent operations as tracking, portrait recognition, and duplicate- removing from plural objects according to the recognition result are facilitated.

[0095] Embodiment 2

[0096] Corresponding to the above method, the present application provides an object positioning method, as shown in Fig. 3, the method comprises:

[0097] 310 - receiving a color image and a depth image to which the color image corresponds;

[0098] 320 - fusing the color image with the depth image to obtain a target image, wherein the target image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image;

[0099] preferably, before fusing the color image with the depth image, the method further comprises:

[0100] 321 - performing an image normalization operation on the depth image according to a preset method and a preset parameter.

[0101] Preferably, before fusing the color image with the depth image, the method further comprises:

[0102] 322 - performing image registration on the normalized depth image and the color image.

[0103] Preferably, the color image is shot by a first camera, the depth image is shot by a second camera, and performing image registration on the color image and the depth image includes:

[0104] employing a checkerboard method to calibrate the first camera and the second camera, and obtaining corresponding transformation matrixes of the first camera and the second camera; and

[0105] performing image registration on the color image and the depth image according to the transformation matrixes.

[0106] 330 - inputting the target image in a preset model for recognition, and positioning a position of a target object in the target image, wherein an input layer of the preset model includes RGB channels and an Alpha channel.

[0107] Preferably, before inputting the target image in a preset model for recognition, the method further comprises:

[0108] 331 - performing data enhancement on the target image.

[0109] Preferably, a process of training the preset model includes:

[0110] 340 - obtaining a training image set, wherein the image set consists of the color image previously marked with a sample target and the depth image to which the color image corresponds;

[0111] performing an image normalization operation on the depth image, and converting the same to a preset format;

[0112] performing image registration on the color image and the corresponding depth image;

[0113] fusing the depth image with the corresponding color image to obtain a testing image, wherein the testing image is the RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and

[0114] taking the testing image as input to a target model, correspondingly taking the previously marked sample target as expected output from the target model, and continually training the target model until the target model satisfies a preset condition.

[0115] Preferably, the target model is obtained by the following mode:

[0116] 341 - modifying an input layer of a Yolov3 model as four channels, and obtaining an improved Yolov3 model, wherein the input layer includes RGB channels and an Alpha channel; and

[0117] clipping a backbone network of the improved Yolov3 model according to a preset clipping parameter, and obtaining the target model.

[0118] Embodiment 3

[0119] Corresponding to the above method, the present application provides an object positioning device, as shown in Fig. 4, the device comprises:

[0120] a receiving module 410, for receiving a color image and a depth image to which the color image corresponds;

[0121] an image processing module 420, for fusing the color image with the depth image to obtain a target image, wherein the target image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and

[0122] a matching module 430, for inputting the target image in a preset model for recognition, and positioning a position of a target object in the target image, wherein an input layer of the preset model includes RGB channels and an Alpha channel.

[0123] Preferably, the image processing module 420 is further usable for performing image registration on the color image and the depth image.

[0124] Preferably, the image processing module 420 is further usable for performing an image normalization process on the depth image according to a preset method and a preset parameter.

[0125] Preferably, the image processing module 420 is further usable for performing image registration on the normalized depth image and the color image.

[0126] Preferably, the color image is shot by a first camera, the depth image is shot by a second camera, and the image processing module 420 is further usable for employing a checkerboard method to calibrate the first camera and the second camera, and obtaining corresponding transformation matrixes of the first camera and the second camera; and for

[0127] performing image registration on the color image and the depth image according to the transformation matrixes.

[0128] Preferably, the image processing module 420 is further useable for performing data enhancement on the target image.

[0129] Preferably, the device further comprises a model training module 430 for obtaining a training image set, wherein the image set consists of the color image previously marked with a sample target and the depth image to which the color image corresponds; for

[0130] performing an image normalization operation on the depth image, and converting the same to a preset format; for

[0131] performing image registration on the color image and the corresponding depth image; for

[0132] fusing the depth image with the corresponding color image to obtain a testing image, wherein the testing image is the RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and for

[0133] taking the testing image as input to a target model, correspondingly taking the previously marked sample target as expected output from the target model, and continually training the target model until the target model satisfies a preset condition.

[0134] Preferably, the model training module 430 is further usable for modifying an input layer of a Yolov3 model as four channels, and obtaining an improved Yolov3 model, wherein the input layer includes RGB channels and an Alpha channel; and for clipping a backbone network of the improved Yolov3 model according to a preset clipping parameter, and obtaining the target model.

[0135] Embodiment 4

[0136] Corresponding to the above method and device, Embodiment 4 of the present application provides a computer system that comprises: one or more processor(s); and a memory, associated with the one or more processor(s), for storing a program instruction that performs the following operations when it is read and executed by the one or more processor(s):

[0137] receiving a color image and a depth image to which the color image corresponds;

[0138] fusing the color image with the depth image to obtain a target image, wherein the target image is an RGBD image whose Alpha channel corresponds to the depth image and whose RGB channels correspond to the color image; and

[0139] inputting the target image in a preset model for recognition, and positioning a position of a target object in the target image.

[0140] Fig. 5 exemplarily illustrates the framework of a computer system that can specifically include a processor 1510, a video display adapter 1511, a magnetic disk driver 1512, an input / output interface 1513, a network interface 1514, and a memory 1520. The processor 1510, the video display adapter 1511, the magnetic disk driver 1512, the input / output interface 1513, the network interface 1514, and the memory 1520 can be communicably connected with one another via a communication bus 1530.

[0141] The processor 1510 can be embodied as a general CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuit(s) for executing relevant program(s) to realize the technical solutions provided by the present application.

[0142] The memory 1520 can be embodied in such a form as an ROM (Read Only Memory), an RAM (Random Access Memory), a static storage device, or a dynamic storage device. The memory 1520 can store an operating system 1521 for controlling the running of a computer system 1500, and a basic input / output system (BIOS) for controlling lower-level operations of the computer system 1500. In addition, the memory 1520 can also store a web browser 1523, a data storage administration system 1524, and an icon font processing system 1525, etc. The icon font processing system 1525 can be an application program that specifically realizes the aforementioned various step operations in the embodiments of the present application. To sum it up, when the technical solutions provided by the present application are to be realized via software or firmware, the relevant program codes are stored in the memory 1520, and invoked and executed by the processor 1510.

[0143] The input / output interface 1513 is employed to connect with an input / output module to realize input and output of information. The input / output module can be equipped in the device as a component part (not shown in the drawings), and can also be externally connected with the device to provide corresponding functions. The input means can include a keyboard, a mouse, a touch screen, a microphone, and various sensors etc., and the output means can include a display screen, a loudspeaker, a vibrator, an indicator light etc.

[0144] The network interface 1514 is employed to connect to a communication module (not shown in the drawings) to realize intercommunication between the current device and other devices. The communication module can realize communication in a wired mode (via USB, network cable, for example) or in a wireless mode (via mobile network, WIFI, Bluetooth, etc.).

[0145] The bus 1530 includes a passageway transmitting information between various component parts of the device (such as the processor 1510, the video display adapter 1511, the magnetic disk driver 1512, the input / output interface 1513, the network interface 1514, and the memory 1520).

[0146] Additionally, the computer system 1500 may further obtain information of specific collection conditions from a virtual resource object collection condition information database 1541 for judgment on conditions, and so on.

[0147] As should be noted, although merely the processor 1510, the video display adapter 1511, the magnetic disk driver 1512, the input / output interface 1513, the network interface 1514, the memory 1520, and the bus 1530 are illustrated for the aforementioned device, the device may further include other component parts prerequisite for realizing normal running during specific implementation. In addition, as can be understood by persons skilled in the art, the aforementioned device may as well only include component parts necessary for realizing the solutions of the present application, without including the entire component parts as illustrated.

[0148] As can be known through the description to the aforementioned embodiments, it is clearly learnt by person skilled in the art that the present application can be realized through software plus a general hardware platform. Based on such understanding, the technical solutions of the present application, or the contributions made thereby over the state of the art, can be essentially embodied in the form of a software product, and such a computer software product can be stored in a storage medium, such as an ROM / RAM, a magnetic disk, an optical disk etc., and includes plural instructions enabling a computer equipment (such as a personal computer, a server, or a network device etc.) to execute the methods described in various embodiments or some sections of the embodiments of the present application.

[0149] The various embodiments are progressively described in the Description, identical or similar sections among the various embodiments can be inferred from one another, and each embodiment stresses what is different from other embodiments. Particularly, with respect to the system or system embodiment, since it is essentially similar to the method embodiment, its description is relatively simple, and the relevant sections thereof can be inferred from the corresponding sections of the method embodiment. The system or system embodiment as described above is merely exemplary in nature, units therein described as separate parts can be or may not be physically separate, parts displayed as units can be or may not be physical units, that is to say, they can be located in a single site, or distributed over a plurality of network units. It is possible to base on practical requirements to select partial modules or the entire modules to realize the objectives of the embodied solutions. It is understandable and implementable by persons ordinarily skilled in the art without spending creative effort in the process.

[0150] What the above describes is merely directed to preferred embodiments of the present invention, and is not meant to restrict the present invention. Any modification, equivalent substitution and improvement makeable within the spirit and scope of the present invention shall all be covered by the protection scope of the present invention.

Claims

<pat:ClaimStatement>CLAIMS< / pat:ClaimStatement> <pat:Claims com:id="claims"> <pat:Claim com:id="CLM-00001"> <pat:ClaimNumber>1< / pat:ClaimNumber> <pat:ClaimText>1. An object positioning system comprising: a receiving module configured to receive a color image comprising red green blue (RGB) channels and a depth image, comprising an Alpha channel, the depth image corresponding to the color image; an image processing module configured to fuse the color image with the depth image to obtain a target image, wherein the target image is a red green blue depth (RGBD) image comprising the Alpha channel corresponding to the depth image and RGB channels corresponding to the color image; and a matching module, for inputting the target image in a preset model for recognition a target object and a position of the target object in the target image, wherein inputting the target image comprises inputting to an input layer of the preset model the RGB channels and the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00002"> <pat:ClaimNumber>2< / pat:ClaimNumber> <pat:ClaimText>2. The system of claim 1, wherein the image processing module is further configured to perform image registration on the color image and the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00003"> <pat:ClaimNumber>3< / pat:ClaimNumber> <pat:ClaimText>3. The system of claim 2, wherein registration of an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00004"> <pat:ClaimNumber>4< / pat:ClaimNumber> <pat:ClaimText>4. The system of any one of claims 1 to 3, wherein the image processing module is further configured to performing an image normalization operation on the depth image according to a preset method and a preset parameter, and converting the same to a picture data type. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00005"> <pat:ClaimNumber>5< / pat:ClaimNumber> <pat:ClaimText>5. The system of claim 4, wherein the picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00006"> <pat:ClaimNumber>6< / pat:ClaimNumber> <pat:ClaimText>6. The system of any one of claims 1 to 5, wherein the preset model is obtained by modifying You Only Look Once (YOLO) Version Three (v3) model to a four channel network the modification being in the input layer to receive the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00007"> <pat:ClaimNumber>7< / pat:ClaimNumber> <pat:ClaimText>7. The system of any one of claims 1 to 6, wherein a backbone network layer is clipped according to a preset clipping parameter for reducing a number of model layers of the preset model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00008"> <pat:ClaimNumber>8< / pat:ClaimNumber> <pat:ClaimText>8. The system of any one of claims 1 to 7, wherein the image processing module is further configured to enhance a data of the target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00009"> <pat:ClaimNumber>9< / pat:ClaimNumber> <pat:ClaimText>9. The system of any one of claims 1 to 7, further comprising a color camera configured to obtain the color image, and a depth camera configured to obtain the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00010"> <pat:ClaimNumber>10< / pat:ClaimNumber> <pat:ClaimText>10. The system of claim 9, wherein the color camera and the depth camera are each positioned a predetermined height and installation angle. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00011"> <pat:ClaimNumber>11< / pat:ClaimNumber> <pat:ClaimText>11. The system of claim 10, wherein the installation angle is perpendicular to the ground. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00012"> <pat:ClaimNumber>12< / pat:ClaimNumber> <pat:ClaimText>12. The system of any one of claims 1 to 11 further comprising a model training module configured to train the preset model by: collecting a training image dataset comprising training color images and corresponding training depth images; marking people contained in each training color image with a visual object classes (VOC) format; converting each training color image from a blue green red (BGR) pattern to an RGB pattern; processing each training depth image normalize the training depth image to a predetermine picture data type; registering the training color image and the corresponding training depth image; obtaining a four channel RGBD training target image for each training color image and corresponding training depth image comprising the Alpha channel of the training depth image and the RGB channels of the training color image; and training the preset model by taking each training target image as input to an improved model and correspondingly taking each marked person as an expected output from the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00013"> <pat:ClaimNumber>13< / pat:ClaimNumber> <pat:ClaimText>13. The system of claim 12, wherein training the preset model further comprises enhancing the training target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00014"> <pat:ClaimNumber>14< / pat:ClaimNumber> <pat:ClaimText>14. The system of any one of claims 12 to 13, wherein 85% of the training image dataset is used for training the model and the remained is used for testing the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00015"> <pat:ClaimNumber>15< / pat:ClaimNumber> <pat:ClaimText>15. The system of any one of claims 12 to 14, wherein the predetermined picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00016"> <pat:ClaimNumber>16< / pat:ClaimNumber> <pat:ClaimText>16. The system of any one of claims 12 to 15 wherein registering an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00017"> <pat:ClaimNumber>17< / pat:ClaimNumber> <pat:ClaimText>17. The system of any one of claims 12 to 16, wherein training the preset model comprises: modifying the training parameter of the model employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends and can be regarded that the model has completed training; and outputting the preset model of the target with the modified training parameter. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00018"> <pat:ClaimNumber>18< / pat:ClaimNumber> <pat:ClaimText>18. An object positioning method comprising: receiving, by a receiving module, a color image comprising red green blue (RGB) channels and a depth image, comprising an Alpha channel, the depth image corresponding to the color image; fusing, by an image processing module, the color image with the depth image to obtain a target image, wherein the target image is a red green blue depth (RGBD) image comprising the Alpha channel corresponding to the depth image and RGB channels corresponding to the color image; and inputting, by a matching module, the target image in a preset model for recognition a target object and a position of the target object in the target image, wherein inputting the target image comprises inputting to an input layer of the preset model the RGB channels and the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00019"> <pat:ClaimNumber>19< / pat:ClaimNumber> <pat:ClaimText>19. The method of claim 18 further comprising performing, by the image processing module image registration on the color image and the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00020"> <pat:ClaimNumber>20< / pat:ClaimNumber> <pat:ClaimText>20. The method of claim 19, wherein registration of an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00021"> <pat:ClaimNumber>21< / pat:ClaimNumber> <pat:ClaimText>21. The method of any one of claims 18 to 20, further comprising performing, by the image processing module, an image normalization operation on the depth image according to a preset method and a preset parameter, and converting the same to a picture data type. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00022"> <pat:ClaimNumber>22< / pat:ClaimNumber> <pat:ClaimText>22. The method of claim 21, wherein the picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00023"> <pat:ClaimNumber>23< / pat:ClaimNumber> <pat:ClaimText>23. The method of any one of claims 18 to 22, wherein the preset model is obtained by modifying You Only Look Once (YOLO) Version Three (v3) model to a four channel network the modification being in the input layer to receive the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00024"> <pat:ClaimNumber>24< / pat:ClaimNumber> <pat:ClaimText>24. The method of any one of claims 18 to 23, wherein a backbone network layer is clipped according to a preset clipping parameter for reducing a number of model layers of the preset model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00025"> <pat:ClaimNumber>25< / pat:ClaimNumber> <pat:ClaimText>25. The method of any one of claims 18 to 24, wherein the image processing module is further configured to enhance a data of the target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00026"> <pat:ClaimNumber>26< / pat:ClaimNumber> <pat:ClaimText>26. The method of any one of claims 18 to 24, further comprising obtaining the color image by a color camera, and obtaining the depth image by a depth camera. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00027"> <pat:ClaimNumber>27< / pat:ClaimNumber> <pat:ClaimText>27. The method of claim 26, wherein the color camera and the depth camera are each positioned a predetermined height and installation angle. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00028"> <pat:ClaimNumber>28< / pat:ClaimNumber> <pat:ClaimText>28. The method of claim 27, wherein the installation angle is perpendicular to the ground. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00029"> <pat:ClaimNumber>29< / pat:ClaimNumber> <pat:ClaimText>29. The method of any one of claims 18 to 28 further comprising training, by a model training module, the preset model, the training comprising: collecting a training image dataset comprising training color images and corresponding training depth images; marking people contained in each training color image with a visual object classes (VOC) format; converting each training color image from a blue green red (BGR) pattern to an RGB pattern; processing each training depth image normalize the training depth image to a predetermine picture data type; registering the training color image and the corresponding training depth image; obtaining a four channel RGBD training target image for each training color image and corresponding training depth image comprising the Alpha channel of the training depth image and the RGB channels of the training color image; and training the preset model by taking each training target image as input to an improved model and correspondingly taking each marked person as an expected output from the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00030"> <pat:ClaimNumber>30< / pat:ClaimNumber> <pat:ClaimText>30. The method of claim 29, wherein training the preset model further comprises enhancing the training target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00031"> <pat:ClaimNumber>31< / pat:ClaimNumber> <pat:ClaimText>31. The method of any one of claims 29 to 30, wherein 85% of the training image dataset is used for training the model and the remained is used for testing the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00032"> <pat:ClaimNumber>32< / pat:ClaimNumber> <pat:ClaimText>32. The method of any one of claims 29 to 31, wherein the predetermined picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00033"> <pat:ClaimNumber>33< / pat:ClaimNumber> <pat:ClaimText>33. The method of any one of claims 29 to 32 wherein registering an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00034"> <pat:ClaimNumber>34< / pat:ClaimNumber> <pat:ClaimText>34. The method of any one of claims 29 to 33, wherein training the preset model comprises: modifying the training parameter of the model employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends and can be regarded that the model has completed training; and outputting the preset model of the target with the modified training parameter. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00035"> <pat:ClaimNumber>35< / pat:ClaimNumber> <pat:ClaimText>35. An object positioning device comprising: a receiving module configured to receive a color image comprising red green blue (RGB) channels and a depth image, comprising an Alpha channel, the depth image corresponding to the color image; an image processing module configured to fuse the color image with the depth image to obtain a target image, wherein the target image is a red green blue depth (RGBD) image comprising the Alpha channel corresponding to the depth image and RGB channels corresponding to the color image; and a matching module, for inputting the target image in a preset model for recognition a target object and a position of the target object in the target image, wherein inputting the target image comprises inputting to an input layer of the preset model the RGB channels and the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00036"> <pat:ClaimNumber>36< / pat:ClaimNumber> <pat:ClaimText>36. The device of claim 35, wherein the image processing module is further configured to perform image registration on the color image and the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00037"> <pat:ClaimNumber>37< / pat:ClaimNumber> <pat:ClaimText>37. The device of claim 36, wherein registration of an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00038"> <pat:ClaimNumber>38< / pat:ClaimNumber> <pat:ClaimText>38. The device of any one of claims 35 to 37, wherein the image processing module is further configured to performing an image normalization operation on the depth image according to a preset method and a preset parameter, and converting the same to a picture data type. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00039"> <pat:ClaimNumber>39< / pat:ClaimNumber> <pat:ClaimText>39. The device of claim 38, wherein the picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00040"> <pat:ClaimNumber>40< / pat:ClaimNumber> <pat:ClaimText>40. The device of any one of claims 35 to 39, wherein the preset model is obtained by modifying You Only Look Once (YOLO) Version Three (v3) model to a four channel network the modification being in the input layer to receive the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00041"> <pat:ClaimNumber>41< / pat:ClaimNumber> <pat:ClaimText>41. The device of any one of claims 35 to 40, wherein a backbone network layer is clipped according to a preset clipping parameter for reducing a number of model layers of the preset model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00042"> <pat:ClaimNumber>42< / pat:ClaimNumber> <pat:ClaimText>42. The device of any one of claims 35 to 41, wherein the image processing module is further configured to enhance a data of the target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00043"> <pat:ClaimNumber>43< / pat:ClaimNumber> <pat:ClaimText>43. The device of any one of claims 35 to 41, further comprising a color camera configured to obtain the color image, and a depth camera configured to obtain the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00044"> <pat:ClaimNumber>44< / pat:ClaimNumber> <pat:ClaimText>44. The device of claim 43, wherein the color camera and the depth camera are each positioned a predetermined height and installation angle. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00045"> <pat:ClaimNumber>45< / pat:ClaimNumber> <pat:ClaimText>45. The device of claim 44, wherein the installation angle is perpendicular to the ground. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00046"> <pat:ClaimNumber>46< / pat:ClaimNumber> <pat:ClaimText>46. The device of any one of claims 35 to 45 further comprising a model training module configured to train the preset model by: collecting a training image dataset comprising training color images and corresponding training depth images; marking people contained in each training color image with a visual object classes (VOC) format; converting each training color image from a blue green red (BGR) pattern to an RGB pattern; processing each training depth image normalize the training depth image to a predetermine picture data type; registering the training color image and the corresponding training depth image; obtaining a four channel RGBD training target image for each training color image and corresponding training depth image comprising the Alpha channel of the training depth image and the RGB channels of the training color image; and training the preset model by taking each training target image as input to an improved model and correspondingly taking each marked person as an expected output from the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00047"> <pat:ClaimNumber>47< / pat:ClaimNumber> <pat:ClaimText>47. The device of claim 46, wherein training the preset model further comprises enhancing the training target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00048"> <pat:ClaimNumber>48< / pat:ClaimNumber> <pat:ClaimText>48. The device of any one of claims 46 to 47, wherein 85% of the training image dataset is used for training the model and the remained is used for testing the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00049"> <pat:ClaimNumber>49< / pat:ClaimNumber> <pat:ClaimText>49. The device of any one of claims 46 to 48, wherein the predetermined picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00050"> <pat:ClaimNumber>50< / pat:ClaimNumber> <pat:ClaimText>50. The device of any one of claims 46 to 49 wherein registering an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00051"> <pat:ClaimNumber>51< / pat:ClaimNumber> <pat:ClaimText>51. The device of any one of claims 46 to 50, wherein training the preset model comprises: modifying the training parameter of the model employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends and can be regarded that the model has completed training; and outputting the preset model of the target with the modified training parameter. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00052"> <pat:ClaimNumber>52< / pat:ClaimNumber> <pat:ClaimText>52. A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, cause the processor to: receive, by a receiving module, a color image comprising red green blue (RGB) channels and a depth image, comprising an Alpha channel, the depth image corresponding to the color image; fuse, by an image processing module, the color image with the depth image to obtain a target image, wherein the target image is a red green blue depth (RGBD) image comprising the Alpha channel corresponding to the depth image and RGB channels corresponding to the color image; and input, by a matching module, the target image in a preset model for recognition a target object and a position of the target object in the target image, wherein inputting the target image comprises inputting to an input layer of the preset model the RGB channels and the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00053"> <pat:ClaimNumber>53< / pat:ClaimNumber> <pat:ClaimText>53. The computer-readable storage medium of claim 52 wherein the processor further performs, by the image processing module, image registration on the color image and the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00054"> <pat:ClaimNumber>54< / pat:ClaimNumber> <pat:ClaimText>54. The computer-readable storage medium of claim 53, wherein registration of an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration computer-readable storage medium; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00055"> <pat:ClaimNumber>55< / pat:ClaimNumber> <pat:ClaimText>55. The computer-readable storage medium of any one of claims 52 to 54, wherein the processor further performs, by the image processing module, an image normalization operation on the depth image according to a preset computer-readable storage medium and a preset parameter, and converting the same to a picture data type. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00056"> <pat:ClaimNumber>56< / pat:ClaimNumber> <pat:ClaimText>56. The computer-readable storage medium of claim 55, wherein the picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00057"> <pat:ClaimNumber>57< / pat:ClaimNumber> <pat:ClaimText>57. The computer-readable storage medium of any one of claims 52 to 56, wherein the preset model is obtained by modifying You Only Look Once (YOLO) Version Three (v3) model to a four channel network the modification being in the input layer to receive the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00058"> <pat:ClaimNumber>58< / pat:ClaimNumber> <pat:ClaimText>58. The computer-readable storage medium of any one of claims 52 to 57, wherein a backbone network layer is clipped according to a preset clipping parameter for reducing a number of model layers of the preset model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00059"> <pat:ClaimNumber>59< / pat:ClaimNumber> <pat:ClaimText>59. The computer-readable storage medium of any one of claims 52 to 58, wherein the image processing module is further configured to enhance a data of the target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00060"> <pat:ClaimNumber>60< / pat:ClaimNumber> <pat:ClaimText>60. The computer-readable storage medium of any one of claims 52 to 58, wherein the processor further obtains the color image by a color camera, and obtains the depth image by a depth camera. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00061"> <pat:ClaimNumber>61< / pat:ClaimNumber> <pat:ClaimText>61. The computer-readable storage medium of claim 60, wherein the color camera and the depth camera are each positioned a predetermined height and installation angle. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00062"> <pat:ClaimNumber>62< / pat:ClaimNumber> <pat:ClaimText>62. The computer-readable storage medium of claim 61, wherein the installation angle is perpendicular to the ground. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00063"> <pat:ClaimNumber>63< / pat:ClaimNumber> <pat:ClaimText>63. The computer-readable storage medium of any one of claims 52 to 62 wherein the processor further trains, by a model training module, the preset model, the training comprising: collecting a training image dataset comprising training color images and corresponding training depth images; marking people contained in each training color image with a visual object classes (VOC) format; converting each training color image from a blue green red (BGR) pattern to an RGB pattern; processing each training depth image normalize the training depth image to a predetermine picture data type; registering the training color image and the corresponding training depth image; obtaining a four channel RGBD training target image for each training color image and corresponding training depth image comprising the Alpha channel of the training depth image and the RGB channels of the training color image; and training the preset model by taking each training target image as input to an improved model and correspondingly taking each marked person as an expected output from the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00064"> <pat:ClaimNumber>64< / pat:ClaimNumber> <pat:ClaimText>64. The computer-readable storage medium of claim 63, wherein training the preset model further comprises enhancing the training target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00065"> <pat:ClaimNumber>65< / pat:ClaimNumber> <pat:ClaimText>65. The computer-readable storage medium of any one of claims 63 to 64, wherein 85% of the training image dataset is used for training the model and the remained is used for testing the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00066"> <pat:ClaimNumber>66< / pat:ClaimNumber> <pat:ClaimText>66. The computer-readable storage medium of any one of claims 63 to 65, wherein the predetermined picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00067"> <pat:ClaimNumber>67< / pat:ClaimNumber> <pat:ClaimText>67. The computer-readable storage medium of any one of claims 63 to 66 wherein registering an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration computer-readable storage medium; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00068"> <pat:ClaimNumber>68< / pat:ClaimNumber> <pat:ClaimText>68. The computer-readable storage medium of any one of claims 63 to 67, wherein training the preset model comprises: modifying the training parameter of the model employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends and can be regarded that the model has completed training; and outputting the preset model of the target with the modified training parameter. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00069"> <pat:ClaimNumber>69< / pat:ClaimNumber> <pat:ClaimText>69. An object positioning system comprising: a receiving module configured to receive a color image comprising red green blue (RGB) channels and a depth image, comprising an Alpha channel, the depth image corresponding to the color image; an image processing module configured to fuse the color image with the depth image to obtain a target image, wherein the target image is a red green blue depth (RGBD) image comprising the Alpha channel corresponding to the depth image and RGB channels corresponding to the color image; a matching module, for inputting the target image in a preset model for recognition a target object and a position of the target object in the target image, wherein inputting the target image comprises inputting to an input layer of the preset model the RGB channels and the Alpha channel; and a model training module configured to train the preset model based on at least one training target image comprising a marked person as an expected outcome. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00070"> <pat:ClaimNumber>70< / pat:ClaimNumber> <pat:ClaimText>70. The system of claim 69, wherein the image processing module is further configured to perform image registration on the color image and the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00071"> <pat:ClaimNumber>71< / pat:ClaimNumber> <pat:ClaimText>71. The system of claim 70, wherein registration of an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00072"> <pat:ClaimNumber>72< / pat:ClaimNumber> <pat:ClaimText>72. The system of any one of claims 69 to 71, wherein the image processing module is further configured to performing an image normalization operation on the depth image according to a preset method and a preset parameter, and converting the same to a picture data type. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00073"> <pat:ClaimNumber>73< / pat:ClaimNumber> <pat:ClaimText>73. The system of claim 72, wherein the picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00074"> <pat:ClaimNumber>74< / pat:ClaimNumber> <pat:ClaimText>74. The system of any one of claims 69 to 73, wherein the preset model is obtained by modifying You Only Look Once (YOLO) Version Three (v3) model to a four channel network the modification being in the input layer to receive the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00075"> <pat:ClaimNumber>75< / pat:ClaimNumber> <pat:ClaimText>75. The system of any one of claims 69 to 74, wherein a backbone network layer is clipped according to a preset clipping parameter for reducing a number of model layers of the preset model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00076"> <pat:ClaimNumber>76< / pat:ClaimNumber> <pat:ClaimText>76. The system of any one of claims 69 to 75, wherein the image processing module is further configured to enhance a data of the target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00077"> <pat:ClaimNumber>77< / pat:ClaimNumber> <pat:ClaimText>77. The system of any one of claims 69 to 75, further comprising a color camera configured to obtain the color image, and a depth camera configured to obtain the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00078"> <pat:ClaimNumber>78< / pat:ClaimNumber> <pat:ClaimText>78. The system of claim 77, wherein the color camera and the depth camera are each positioned a predetermined height and installation angle. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00079"> <pat:ClaimNumber>79< / pat:ClaimNumber> <pat:ClaimText>79. The system of claim 78, wherein the installation angle is perpendicular to the ground. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00080"> <pat:ClaimNumber>80< / pat:ClaimNumber> <pat:ClaimText>80. The system of any one of claims 69 to 79, wherein the model training module is further configured to train the preset model by: collecting a training image dataset comprising training color images and corresponding training depth images; marking people contained in each training color image with a visual object classes (VOC) format; converting each training color image from a blue green red (BGR) pattern to an RGB pattern; processing each training depth image normalize the training depth image to a predetermine picture data type; registering the training color image and the corresponding training depth image; obtaining a four channel RGBD training target image for each training color image and corresponding training depth image comprising the Alpha channel of the training depth image and the RGB channels of the training color image; and training the preset model by taking each training target image as input to an improved model and correspondingly taking each marked person as an expected output from the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00081"> <pat:ClaimNumber>81< / pat:ClaimNumber> <pat:ClaimText>81. The system of claim 80, wherein training the preset model further comprises enhancing the training target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00082"> <pat:ClaimNumber>82< / pat:ClaimNumber> <pat:ClaimText>82. The system of any one of claims 80 to 81, wherein 85% of the training image dataset is used for training the model and the remained is used for testing the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00083"> <pat:ClaimNumber>83< / pat:ClaimNumber> <pat:ClaimText>83. The system of any one of claims 80 to 82, wherein the predetermined picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00084"> <pat:ClaimNumber>84< / pat:ClaimNumber> <pat:ClaimText>84. The system of any one of claims 80 to 83 wherein registering an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00085"> <pat:ClaimNumber>85< / pat:ClaimNumber> <pat:ClaimText>85. The system of any one of claims 80 to 84, wherein training the preset model comprises: modifying the training parameter of the model employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends and can be regarded that the model has completed training; and outputting the preset model of the target with the modified training parameter. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00086"> <pat:ClaimNumber>86< / pat:ClaimNumber> <pat:ClaimText>86. An object positioning method comprising: receiving, by a receiving module, a color image comprising red green blue (RGB) channels and a depth image, comprising an Alpha channel, the depth image corresponding to the color image; fusing, by an image processing module, the color image with the depth image to obtain a target image, wherein the target image is a red green blue depth (RGBD) image comprising the Alpha channel corresponding to the depth image and RGB channels corresponding to the color image; inputting, by a matching module, the target image in a preset model for recognition a target object and a position of the target object in the target image, wherein inputting the target image comprises inputting to an input layer of the preset model the RGB channels and the Alpha channel; and training, by a model training module, the preset model based on at least one training target image comprising a marked person as an expected outcome. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00087"> <pat:ClaimNumber>87< / pat:ClaimNumber> <pat:ClaimText>87. The method of claim 86 further comprising performing, by the image processing module image registration on the color image and the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00088"> <pat:ClaimNumber>88< / pat:ClaimNumber> <pat:ClaimText>88. The method of claim 87, wherein registration of an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00089"> <pat:ClaimNumber>89< / pat:ClaimNumber> <pat:ClaimText>89. The method of any one of claims 86 to 88, further comprising performing, by the image processing module, an image normalization operation on the depth image according to a preset method and a preset parameter, and converting the same to a picture data type. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00090"> <pat:ClaimNumber>90< / pat:ClaimNumber> <pat:ClaimText>90. The method of claim 89, wherein the picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00091"> <pat:ClaimNumber>91< / pat:ClaimNumber> <pat:ClaimText>91. The method of any one of claims 86 to 90, wherein the preset model is obtained by modifying You Only Look Once (YOLO) Version Three (v3) model to a four channel network the modification being in the input layer to receive the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00092"> <pat:ClaimNumber>92< / pat:ClaimNumber> <pat:ClaimText>92. The method of any one of claims 86 to 91, wherein a backbone network layer is clipped according to a preset clipping parameter for reducing a number of model layers of the preset model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00093"> <pat:ClaimNumber>93< / pat:ClaimNumber> <pat:ClaimText>93. The method of any one of claims 86 to 92, wherein the image processing module is further configured to enhance a data of the target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00094"> <pat:ClaimNumber>94< / pat:ClaimNumber> <pat:ClaimText>94. The method of any one of claims 86 to 92, further comprising obtaining the color image by a color camera, and obtaining the depth image by a depth camera. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00095"> <pat:ClaimNumber>95< / pat:ClaimNumber> <pat:ClaimText>95. The method of claim 94, wherein the color camera and the depth camera are each positioned a predetermined height and installation angle. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00096"> <pat:ClaimNumber>96< / pat:ClaimNumber> <pat:ClaimText>96. The method of claim 95, wherein the installation angle is perpendicular to the ground. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00097"> <pat:ClaimNumber>97< / pat:ClaimNumber> <pat:ClaimText>97. The method of any one of claims 86 to 96 further comprising training, by a model training module, the preset model, the training comprising: collecting a training image dataset comprising training color images and corresponding training depth images; marking people contained in each training color image with a visual object classes (VOC) format; converting each training color image from a blue green red (BGR) pattern to an RGB pattern; processing each training depth image normalize the training depth image to a predetermine picture data type; registering the training color image and the corresponding training depth image; obtaining a four channel RGBD training target image for each training color image and corresponding training depth image comprising the Alpha channel of the training depth image and the RGB channels of the training color image; and training the preset model by taking each training target image as input to an improved model and correspondingly taking each marked person as an expected output from the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00098"> <pat:ClaimNumber>98< / pat:ClaimNumber> <pat:ClaimText>98. The method of claim 97, wherein training the preset model further comprises enhancing the training target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00099"> <pat:ClaimNumber>99< / pat:ClaimNumber> <pat:ClaimText>99. The method of any one of claims 97 to 98, wherein 85% of the training image dataset is used for training the model and the remained is used for testing the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00100"> <pat:ClaimNumber>100< / pat:ClaimNumber> <pat:ClaimText>100. The method of any one of claims 97 to 99, wherein the predetermined picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00101"> <pat:ClaimNumber>101< / pat:ClaimNumber> <pat:ClaimText>101. The method of any one of claims 97 to 100 wherein registering an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00102"> <pat:ClaimNumber>102< / pat:ClaimNumber> <pat:ClaimText>102. The method of any one of claims 97 to 101, wherein training the preset model comprises: modifying the training parameter of the model employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends and can be regarded that the model has completed training; and outputting the preset model of the target with the modified training parameter. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00103"> <pat:ClaimNumber>103< / pat:ClaimNumber> <pat:ClaimText>103. An object positioning device comprising: a receiving module configured to receive a color image comprising red green blue (RGB) channels and a depth image, comprising an Alpha channel, the depth image corresponding to the color image; an image processing module configured to fuse the color image with the depth image to obtain a target image, wherein the target image is a red green blue depth (RGBD) image comprising the Alpha channel corresponding to the depth image and RGB channels corresponding to the color image; and a matching module configured to input the target image in a preset model for recognition a target object and a position of the target object in the target image, wherein inputting the target image comprises inputting to an input layer of the preset model the RGB channels and the Alpha channel; and a model training module configured to train the preset model based on at least one training target image comprising a marked person as an expected outcome. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00104"> <pat:ClaimNumber>104< / pat:ClaimNumber> <pat:ClaimText>104. The device of claim 103, wherein the image processing module is further configured to perform image registration on the color image and the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00105"> <pat:ClaimNumber>105< / pat:ClaimNumber> <pat:ClaimText>105. The device of claim 104, wherein registration of an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00106"> <pat:ClaimNumber>106< / pat:ClaimNumber> <pat:ClaimText>106. The device of any one of claims 103 to 105, wherein the image processing module is further configured to performing an image normalization operation on the depth image according to a preset method and a preset parameter, and converting the same to a picture data type. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00107"> <pat:ClaimNumber>107< / pat:ClaimNumber> <pat:ClaimText>107. The device of claim 106, wherein the picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00108"> <pat:ClaimNumber>108< / pat:ClaimNumber> <pat:ClaimText>108. The device of any one of claims 103 to 107, wherein the preset model is obtained by modifying You Only Look Once (YOLO) Version Three (v3) model to a four channel network the modification being in the input layer to receive the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00109"> <pat:ClaimNumber>109< / pat:ClaimNumber> <pat:ClaimText>109. The device of any one of claims 103 to 108, wherein a backbone network layer is clipped according to a preset clipping parameter for reducing a number of model layers of the preset model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00110"> <pat:ClaimNumber>110< / pat:ClaimNumber> <pat:ClaimText>110. The device of any one of claims 103 to 109, wherein the image processing module is further configured to enhance a data of the target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00111"> <pat:ClaimNumber>111< / pat:ClaimNumber> <pat:ClaimText>111. The device of any one of claims 103 to 109, wherein the further comprising a color camera configured to obtain the color image, and a depth camera configured to obtain the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00112"> <pat:ClaimNumber>112< / pat:ClaimNumber> <pat:ClaimText>112. The device of claim 111, wherein the color camera and the depth camera are each positioned a predetermined height and installation angle. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00113"> <pat:ClaimNumber>113< / pat:ClaimNumber> <pat:ClaimText>113. The device of claim 112, wherein the installation angle is perpendicular to the ground. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00114"> <pat:ClaimNumber>114< / pat:ClaimNumber> <pat:ClaimText>114. The device of any one of claims 103 to 113 further comprising a model training module configured to train the preset model by: collecting a training image dataset comprising training color images and corresponding training depth images; marking people contained in each training color image with a visual object classes (VOC) format; converting each training color image from a blue green red (BGR) pattern to an RGB pattern; processing each training depth image normalize the training depth image to a predetermine picture data type; registering the training color image and the corresponding training depth image; obtaining a four channel RGBD training target image for each training color image and corresponding training depth image comprising the Alpha channel of the training depth image and the RGB channels of the training color image; and training the preset model by taking each training target image as input to an improved model and correspondingly taking each marked person as an expected output from the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00115"> <pat:ClaimNumber>115< / pat:ClaimNumber> <pat:ClaimText>115. The device of claim 114, wherein training the preset model further comprises enhancing the training target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00116"> <pat:ClaimNumber>116< / pat:ClaimNumber> <pat:ClaimText>116. The device of any one of claims 114 to 115, wherein 85% of the training image dataset is used for training the model and the remained is used for testing the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00117"> <pat:ClaimNumber>117< / pat:ClaimNumber> <pat:ClaimText>117. The device of any one of claims 114 to 116, wherein the predetermined picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00118"> <pat:ClaimNumber>118< / pat:ClaimNumber> <pat:ClaimText>118. The device of any one of claims 114 to 117 wherein registering an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration method; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00119"> <pat:ClaimNumber>119< / pat:ClaimNumber> <pat:ClaimText>119. The device of any one of claims 114 to 118, wherein training the preset model comprises: modifying the training parameter of the model employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends and can be regarded that the model has completed training; and outputting the preset model of the target with the modified training parameter. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00120"> <pat:ClaimNumber>120< / pat:ClaimNumber> <pat:ClaimText>120. A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, cause the processor to: receive, by a receiving module, a color image comprising red green blue (RGB) channels and a depth image, comprising an Alpha channel, the depth image corresponding to the color image; fuse, by an image processing module, the color image with the depth image to obtain a target image, wherein the target image is a red green blue depth (RGBD) image comprising the Alpha channel corresponding to the depth image and RGB channels corresponding to the color image; input, by a matching module, the target image in a preset model for recognition a target object and a position of the target object in the target image, wherein inputting the target image comprises inputting to an input layer of the preset model the RGB channels and the Alpha channel; and train, by a model training module, the preset model based on at least one training target image comprising a marked person as an expected outcome. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00121"> <pat:ClaimNumber>121< / pat:ClaimNumber> <pat:ClaimText>121. The computer-readable storage medium of claim 120 wherein the processor further performs, by the image processing module, image registration on the color image and the depth image. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00122"> <pat:ClaimNumber>122< / pat:ClaimNumber> <pat:ClaimText>122. The computer-readable storage medium of claim 121, wherein registration of an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration computer-readable storage medium; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00123"> <pat:ClaimNumber>123< / pat:ClaimNumber> <pat:ClaimText>123. The computer-readable storage medium of any one of claims 120 to 122, wherein the processor further performs, by the image processing module, an image normalization operation on the depth image according to a preset computer-readable storage medium and a preset parameter, and converting the same to a picture data type. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00124"> <pat:ClaimNumber>124< / pat:ClaimNumber> <pat:ClaimText>124. The computer-readable storage medium of claim 123, wherein the picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00125"> <pat:ClaimNumber>125< / pat:ClaimNumber> <pat:ClaimText>125. The computer-readable storage medium of any one of claims 120 to 124, wherein the preset model is obtained by modifying You Only Look Once (YOLO) Version Three (v3) model to a four channel network the modification being in the input layer to receive the Alpha channel. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00126"> <pat:ClaimNumber>126< / pat:ClaimNumber> <pat:ClaimText>126. The computer-readable storage medium of any one of claims 120 to 125, wherein a backbone network layer is clipped according to a preset clipping parameter for reducing a number of model layers of the preset model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00127"> <pat:ClaimNumber>127< / pat:ClaimNumber> <pat:ClaimText>127. The computer-readable storage medium of any one of claims 120 to 126, wherein the image processing module is further configured to enhance a data of the target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00128"> <pat:ClaimNumber>128< / pat:ClaimNumber> <pat:ClaimText>128. The computer-readable storage medium of any one of claims 120 to 126, wherein the processor further obtains the color image by a color camera, and obtains the depth image by a depth camera. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00129"> <pat:ClaimNumber>129< / pat:ClaimNumber> <pat:ClaimText>129. The computer-readable storage medium of claim 128, wherein the color camera and the depth camera are each positioned a predetermined height and installation angle. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00130"> <pat:ClaimNumber>130< / pat:ClaimNumber> <pat:ClaimText>130. The computer-readable storage medium of claim 129, wherein the installation angle is perpendicular to the ground. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00131"> <pat:ClaimNumber>131< / pat:ClaimNumber> <pat:ClaimText>131. The computer-readable storage medium of any one of claims 120 to 130 wherein the processor further trains, by a model training module, the preset model, the training comprising: collecting a training image dataset comprising training color images and corresponding training depth images; marking people contained in each training color image with a visual object classes (VOC) format; converting each training color image from a blue green red (BGR) pattern to an RGB pattern; processing each training depth image normalize the training depth image to a predetermine picture data type; registering the training color image and the corresponding training depth image; obtaining a four channel RGBD training target image for each training color image and corresponding training depth image comprising the Alpha channel of the training depth image and the RGB channels of the training color image; and training the preset model by taking each training target image as input to an improved model and correspondingly taking each marked person as an expected output from the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00132"> <pat:ClaimNumber>132< / pat:ClaimNumber> <pat:ClaimText>132. The computer-readable storage medium of claim 131, wherein training the preset model further comprises enhancing the training target image by one or more of image clipping, image size adjusting, image rotational angle adjusting, image luminance and contrast adjusting. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00133"> <pat:ClaimNumber>133< / pat:ClaimNumber> <pat:ClaimText>133. The computer-readable storage medium of any one of claims 131 to 132, wherein 85% of the training image dataset is used for training the model and the remained is used for testing the model. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00134"> <pat:ClaimNumber>134< / pat:ClaimNumber> <pat:ClaimText>134. The computer-readable storage medium of any one of claims 131 to 133, wherein the predetermined picture data type is a unit8 format. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00135"> <pat:ClaimNumber>135< / pat:ClaimNumber> <pat:ClaimText>135. The computer-readable storage medium of any one of claims 131 to 134 wherein registering an image comprises: calculating an internal reference matrix of a camera that shot the image employing a checkerboard calibration computer-readable storage medium; calculating a corresponding transformation matrix according to the internal reference matrix; and performing image registration on the image according to the transformation matrix. < / pat:ClaimText> < / pat:Claim> <pat:Claim com:id="CLM-00136"> <pat:ClaimNumber>136< / pat:ClaimNumber> <pat:ClaimText>136. The computer-readable storage medium of any one of claims 131 to 135, wherein training the preset model comprises: modifying the training parameter of the model employing a stochastic gradient descent algorithm to persistently observe descending circumstance of a loss function Loss of the model until the value of the loss function Loss no longer descends and can be regarded that the model has completed training; and outputting the preset model of the target with the modified training parameter. < / pat:ClaimText> < / pat:Claim> < / pat:Claims>