Lost article retrieving method, system and equipment and storage medium

Abstract

The invention discloses a lost article retrieval method, system and device, and a storage medium, and the key points of the technical scheme are that feature extraction is performed on a received lost article image to obtain a first feature vector; obtaining video data including a lost article and at least one person, and extracting a second feature vector of each person in the video data; calculating the compactness between the lost article and the corresponding person according to the first feature vector and each second feature vector; according to the video data, obtaining a lost article position and each person position, and according to the lost article position and each person position, calculating a position relation metric value of the lost article and the corresponding person; and according to the closeness and the position relation metric value corresponding to each person, calculating a corresponding comprehensive relation metric value, and taking the person with the maximum comprehensive relation metric value as a loser. The loser can be actively found, the storage position occupied by the lost article is reduced, the loss of the loser is reduced, and resource waste is avoided.

Description

Technical Field

[0001] The present invention belongs to the field of image processing technology, and in particular relates to a lost property recovery method, system, device and storage medium. Background Art

[0002] In real life, it is very easy for the items we carry with us to get lost. For example, headphones, umbrellas, water cups, etc. are left behind after being placed carelessly and become lost property. Generally, lost items are usually sent to the lost and found office.

[0003] However, a large number of lost items at lost and found offices are not claimed by their owners, resulting in an increasing number of lost items. More and more space is needed to store lost items, and the owners have to spend money to buy their lost items again, resulting in a waste of resources. Summary of the invention

[0004] The purpose of the present invention is to provide a lost property recovery method, system, device and storage medium, which can actively find the owner, reduce the storage space occupied by the lost property, reduce the loss of the owner, and avoid waste of resources.

[0005] A first aspect of the present invention provides a method for recovering lost property, comprising:

[0006] Perform feature extraction on the received lost property image to obtain a first feature vector;

[0007] Acquire video data containing lost items and at least one person, and extract a second feature vector of each person in the video data;

[0008] Calculate the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector;

[0009] Acquire the position of the lost object and the positions of each person according to the video data, and calculate the position relationship measurement value between the lost object and the corresponding person according to the position of the lost object and the positions of each person;

[0010] According to the closeness and position relationship measurement values ​​corresponding to each of the characters, the corresponding comprehensive relationship measurement value is calculated, and the person with the largest comprehensive relationship measurement value is taken as the owner.

[0011] In some embodiments, the calculating the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector includes:

[0012] Substitute the first eigenvector and each second eigenvector into a compactness calculation formula to calculate the corresponding compactness. The compactness calculation formula is:

[0013]

[0014] Wherein, X represents the first eigenvector, and P i represents the second eigenvector, the ||X|| represents the norm of the first eigenvector, the ||P i || represents the norm of the second eigenvector.

[0015] In some embodiments, the calculating of the position relationship measurement value between the lost object and the corresponding person according to the lost object position and the positions of each person includes:

[0016] Substitute the lost property location and the location of each person into the position relationship metric value calculation formula to calculate the corresponding position relationship metric value. The position relationship metric value calculation formula is:

[0017]

[0018] Among them, (x0, y0) represents the location of the lost object. Represents the position of the i-th person.

[0019] In some embodiments, the calculating of the corresponding comprehensive relationship metric value according to the closeness and position relationship metric values ​​corresponding to each of the characters includes:

[0020] Substitute each of the closeness and position relationship measurement values ​​into the comprehensive relationship measurement value calculation formula to calculate the corresponding comprehensive relationship measurement value, and the comprehensive relationship measurement value calculation formula is:

[0021] S i =β*cosine_sim(X,P i )-α*D i ,

[0022] Wherein, α represents the position weight, β represents the feature weight, and D i Indicates the closeness corresponding to the i-th person, the cosine_sim(X, P i ) represents the position relationship measurement value corresponding to the i-th person.

[0023] In some embodiments, obtaining the location of the lost item and the locations of each person according to the video data includes:

[0024] Identify the outline of the lost object and the outlines of each person according to the video data;

[0025] Determine the lost property center according to the lost property outline, and use the lost property center as the lost property location;

[0026] The corresponding character center is determined according to each character outline, and each character center is used as the corresponding character position.

[0027] In some embodiments, obtaining video data containing the lost property and at least one person includes:

[0028] Obtaining the original video and the time when the lost property was picked up, wherein the original video is the video of the place where the lost property was picked up;

[0029] Subtract the preset time period from the lost property picking up time to obtain the interception time;

[0030] Starting from the interception time, video frames are selected in sequence forward along the time sequence, and lost object recognition is performed on the video frames until there is no lost object in the selected video frames, and the selection is stopped to obtain a first video clip containing the lost object;

[0031] Perform person recognition on the video frames in the first video segment, select the video frames with people, obtain the second video segment, and acquire video data containing the lost property and at least one person.

[0032] A second aspect of the present invention provides a lost property recovery system, comprising:

[0033] A first extraction module, used for extracting features from the received lost property image to obtain a first feature vector;

[0034] A second extraction module, used to obtain video data containing lost objects and at least one person, and extract a second feature vector of each person in the video data;

[0035] A first calculation module, used for calculating the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector;

[0036] A second calculation module is used to obtain the position of the lost object and the positions of each person according to the video data, and calculate the position relationship measurement value between the lost object and the corresponding person according to the position of the lost object and the positions of each person;

[0037] The comprehensive calculation module is used to calculate the corresponding comprehensive relationship measurement value according to the closeness and position relationship measurement values ​​corresponding to each of the characters, and take the character with the largest comprehensive relationship measurement value as the owner.

[0038] In some embodiments, the first computing module includes:

[0039] A compactness calculation unit is used to substitute the first eigenvector and each second eigenvector into a compactness calculation formula to calculate the corresponding compactness, and the compactness calculation formula is:

[0040]

[0041] Wherein, X represents the first eigenvector, Pi represents the second eigenvector, ||X|| represents the norm of the first eigenvector, and ||P i || represents the norm of the second eigenvector.

[0042] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0043] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0044] The technical solution provided by the present invention has the following advantages and effects: by extracting the first feature vector of the lost object image and the second feature vector of each person, and calculating the closeness of the two, then calculating the position relationship measurement value between the lost object and each person in the video data, combining the closeness and the relationship measurement value to obtain a comprehensive relationship measurement value, and defining the person with the highest comprehensive relationship measurement value as the owner, so as to actively find the owner, reduce the occupation of the storage location by the lost object, reduce the loss of the owner, and avoid waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the process of the lost property recovery method provided by the present invention;

[0046] Figure 2 It is a structural block diagram of the lost property recovery system provided by the present invention;

[0047] Figure 3 It is a diagram of the internal structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to facilitate the understanding of the present invention, specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0049] Unless otherwise specified or defined, the "first, second..." used in this article is merely used to distinguish names and does not represent a specific quantity or order.

[0050] Unless specifically stated or defined otherwise, the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0051] It should be noted that, in this document, “fixed to” or “connected to” may mean directly fixing or connecting to an element, or indirectly fixing or connecting to an element.

[0052] like Figure 1As shown, this embodiment provides a lost property recovery method, including the following steps S1 to S5:

[0053] Step S1: extract features from the received lost property image to obtain a first feature vector.

[0054] In practical applications, lost objects may be personal belongings such as headphones, mobile phones, water cups, umbrellas, etc. The lost object image is obtained by photographing the lost object with a camera. Images of the lost object at multiple different angles may be photographed, such as the front, side, and back of the lost object. Then, a trained lost object recognition model is used to extract the first feature vector of the lost object. Specifically, the lost object recognition model may use the Faster R-CNN pre-trained model. The Faster R-CNN pre-trained model is pre-trained with an object training set and trained in advance. The object training set includes a large number of photos of different objects at different angles. After the Faster R-CNN pre-trained model is trained until the model converges or reaches the number of iterations, the Faster R-CNN pre-trained model is tested with an object test set. The object test set includes a large number of photos of different objects at different angles. After the test result meets the expected effect, the tested Faster R-CNN pre-trained model is used as the lost object recognition model.

[0055] Step S2: Obtain video data containing the lost property and at least one person, and extract a second feature vector of each person in the video data.

[0056] Specifically, obtaining the video data containing the lost property and at least one person includes:

[0057] Obtaining the original video and the time when the lost property was picked up, wherein the original video is the video of the place where the lost property was picked up;

[0058] Subtract the preset time period from the lost property picking up time to obtain the interception time;

[0059] Starting from the interception time, video frames are selected in sequence forward along the time sequence, and lost object recognition is performed on the video frames until there is no lost object in the selected video frames, and the selection is stopped to obtain a first video clip containing the lost object;

[0060] Perform person recognition on the video frames in the first video segment, select the video frames with people, obtain the second video segment, and acquire video data containing the lost property and at least one person.

[0061] Specifically, the original video may be surveillance time or mobile phone video. After the lost property is picked up, the surveillance video of the place where the lost property was picked up can be retrieved. The time when the lost property was picked up can be learned by asking the person who picked up the lost property. In order to avoid misidentifying the person who picked up the lost property as the owner in the subsequent identification process, and to reduce the subsequent identification of the person who picked up the lost property, feature vector extraction, closeness calculation and position relationship measurement value calculation, a preset time period is set, such as 30 seconds, 1 minute, 1 minute and 30 seconds, 2 minutes, etc. For example, if the lost property is picked up in the cafeteria at 10 o'clock on the same day, the preset time period is 1 minute, and the capture time is 9:59. Video frames are selected from the surveillance video of the cafeteria from 9:59 onwards, and the video frames are identified by the lost property recognition model. If the lost property is not identified in the selected video frames, the selection is stopped to obtain multiple video frames containing the lost property to form a first video clip, and then the video frames of the first video clip are subjected to person recognition to obtain multiple video frames containing the lost property and the person to form a second video clip. For character recognition and feature vector extraction, a character recognition model can be used for feature extraction and recognition. The character recognition model can also be obtained after a large amount of training using the Faster R-CNN pre-trained model. Before training, a large number of character images of different characters at different angles are collected, and then the character images are divided into a character training set and a character test set. The Faster R-CNN pre-trained model is trained with the character training set until the model converges or reaches the number of iterations, and then the Faster R-CNN pre-trained model is tested with the character test set. If the test result meets the expected effect, the Faster R-CNN pre-trained model that has been tested is used as the character recognition model.

[0062] Step S3: Calculate the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector.

[0063] Specifically, the calculating the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector includes:

[0064] Substitute the first eigenvector and each second eigenvector into a compactness calculation formula to calculate the corresponding compactness. The compactness calculation formula is:

[0065]

[0066] Wherein, X represents the first eigenvector, and P i represents the second eigenvector, the ||X|| represents the norm of the first eigenvector, the ||P i || represents the norm of the second eigenvector.

[0067] In practical applications, before the lost property is lost, the owner usually carries the lost property with him / her. The first eigenvector of the lost property is identified by the lost property recognition model, and the corresponding second eigenvector is identified by the person recognition model. The closeness of the first eigenvector and the second eigenvector is calculated by the closeness calculation formula. When the owner carries the lost property with him / her, the closeness of the second eigenvector of the owner and the first eigenvector is higher than the closeness of the second eigenvector and the first eigenvector of other persons. Therefore, the accuracy of identifying the lost owner from various persons can be improved by calculating the closeness of the first eigenvector and the second eigenvector.

[0068] Step S4: obtaining the position of the lost object and the positions of each person according to the video data, and calculating a position relationship measurement value between the lost object and the corresponding person according to the position of the lost object and the positions of each person.

[0069] Specifically, obtaining the location of the lost item and the locations of each person according to the video data includes:

[0070] Identify the outline of the lost object and the outlines of each person according to the video data;

[0071] Determine the lost property center according to the lost property outline, and use the lost property center as the lost property location;

[0072] The corresponding character center is determined according to each character outline, and each character center is used as the corresponding character position.

[0073] In practical applications, a lost object recognition model and a person recognition model are used to recognize each frame of the video data, and the lost object contour and each person contour in each frame of the image are obtained. Multiple first coordinates of the lost object contour and multiple second coordinates of each person contour are extracted. The coordinate calculation method of the lost object center includes:

[0074]

[0075] Among them, (x i ,y i ) represents the i-th first coordinate, n represents the number of first coordinates, and (x0, y0) represents the center of the lost object, that is, the location of the lost object.

[0076] The coordinate calculation method of the center of the character includes:

[0077]

[0078] Among them, (x ij ,y ij ) represents the jth second coordinate of the i-th person, m represents the number of first coordinates, Represents the center of the i-th character, that is, the position of the i-th character.

[0079] Specifically, the calculating of the position relationship measurement value between the lost object and the corresponding person according to the lost object position and the positions of each person includes:

[0080] Substitute the lost property location and the location of each person into the position relationship metric value calculation formula to calculate the corresponding position relationship metric value. The position relationship metric value calculation formula is:

[0081]

[0082] Among them, (x0, y0) represents the location of the lost object. Represents the position of the i-th person.

[0083] In practical applications, before the lost property is lost, the owner usually carries the lost property with him / her. The absolute distance between the location of the lost property and the locations of various people is calculated using the position relationship measurement value calculation formula. When the owner carries the lost property with him / her, the distance between the lost property and the owner is the shortest compared to the distance between the lost property and other people. Therefore, by calculating the position relationship measurement value between the location of the lost property and the locations of various people, the accuracy of identifying the owner from various people can be further improved.

[0084] Step S5: Calculate the corresponding comprehensive relationship measurement value according to the closeness and position relationship measurement values ​​corresponding to each of the characters, and take the character with the largest comprehensive relationship measurement value as the owner.

[0085] Specifically, the calculating of the corresponding comprehensive relationship measurement value according to the closeness and position relationship measurement values ​​corresponding to each of the characters includes:

[0086] Substitute each of the closeness and position relationship measurement values ​​into the comprehensive relationship measurement value calculation formula to calculate the corresponding comprehensive relationship measurement value, and the comprehensive relationship measurement value calculation formula is:

[0087] S i =β*cosine_sim(X,P i )-α*D i ,

[0088] Wherein, α represents the position weight, β represents the feature weight, and D i Indicates the closeness corresponding to the i-th person, the cosine_sim(X, P i ) represents the position relationship measurement value corresponding to the i-th person.

[0089] In actual applications, the comprehensive relationship metric comprehensively considers the closeness and position relationship metrics. The person with the highest comprehensive relationship metric indicates that the person has the closest connection with the lost item and has the highest probability of being the owner. Therefore, the person with the largest comprehensive relationship metric is regarded as the owner, and then the owner is contacted to retrieve the lost item, reducing the occupation of the storage location by the lost item, reducing the loss of the owner, and avoiding resource waste. The loss scene and size of the lost item are different. The loss scene and the size of the lost item will affect the weight of the closeness and position. Therefore, the position weight and feature weight can be dynamically adjusted according to the actual prediction result accuracy. For example, the initial position weight is set to 0.5 and the feature weight is set to 0.5. If the actual prediction result accuracy does not reach the expected accuracy, if the size of the lost item is not greater than the preset volume, the position weight can be adjusted to 0.7 and the feature weight can be adjusted to 0.3. If the size of the lost item is greater than the preset volume, the position weight can be adjusted to 0.4 and the feature weight can be adjusted to 0.6 to improve the accuracy of finding the owner of different lost items in different scenarios.

[0090] The lost property recovery method of the present invention extracts the first feature vector of the lost property image and the second feature vector of each person, calculates the closeness of the two, and then calculates the position relationship measurement value between the lost property and each person in the video data, combines the closeness and the relationship measurement value to obtain a comprehensive relationship measurement value, and determines the person with the highest comprehensive relationship measurement value as the owner, so as to actively find the owner, reduce the occupation of the storage location by the lost property, reduce the loss of the owner, and avoid waste of resources.

[0091] like Figure 2 As shown, the embodiment of the present invention also provides a lost property recovery system, including:

[0092] A first extraction module 10 is used to extract features from the received lost property image to obtain a first feature vector;

[0093] A second extraction module 20, for acquiring video data containing lost items and at least one person, and extracting a second feature vector of each person in the video data;

[0094] A first calculation module 30, used for calculating the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector;

[0095] A second calculation module 40 is used to obtain the position of the lost object and the positions of each person according to the video data, and calculate the position relationship measurement value between the lost object and the corresponding person according to the position of the lost object and the positions of each person;

[0096] The comprehensive calculation module 50 is used to calculate the corresponding comprehensive relationship measurement value according to the closeness and position relationship measurement values ​​corresponding to each of the characters, and take the character with the largest comprehensive relationship measurement value as the owner.

[0097] As an optional implementation, the second extraction module includes the following units (not shown):

[0098] An acquisition unit, used to acquire an original video and a time when the lost item was picked up, wherein the original video is a video of the place where the lost item was picked up;

[0099] A time determination unit, used to subtract a preset time period from the lost property picking-up time to obtain a pick-up time;

[0100] A first selection unit is used to select video frames in sequence from the interception time forward along the time sequence, and perform lost object recognition on the video frames until the selected video frames contain no lost objects and stop selecting, thereby obtaining a first video clip containing the lost objects;

[0101] The second selection unit is used to perform person recognition on the video frames in the first video clip, select the video frames with people, obtain the second video clip, and acquire video data containing the lost property and at least one person.

[0102] As an optional implementation, the first calculation module includes the following units (not shown):

[0103] A compactness calculation unit is used to substitute the first eigenvector and each second eigenvector into a compactness calculation formula to calculate the corresponding compactness, and the compactness calculation formula is:

[0104]

[0105] Wherein, X represents the first eigenvector, Pi represents the second eigenvector, ||X|| represents the norm of the first eigenvector, and ||P i || represents the norm of the second eigenvector.

[0106] In some embodiments, the second computing module includes the following units (not shown):

[0107] The position measurement value calculation unit is used to substitute the lost property position and the positions of each person into the position relationship measurement value calculation formula to calculate the corresponding position relationship measurement value. The position relationship measurement value calculation formula is:

[0108]

[0109] Among them, (x0, y0) represents the location of the lost object. Represents the position of the i-th person.

[0110] In some embodiments, the second computing module further includes the following units (not shown):

[0111] A contour recognition unit, used to recognize the contour of the lost object and the contours of each person according to the video data;

[0112] A first position determination unit, configured to determine a lost property center according to the lost property outline, and use the lost property center as the lost property position;

[0113] The second position determination unit is used to determine the corresponding character center according to each character outline, and use each character center as the corresponding character position.

[0114] In some embodiments, the comprehensive calculation module includes the following units not shown:

[0115] The comprehensive metric value calculation unit is used to substitute each of the closeness and position relationship metric values ​​into the comprehensive relationship metric value calculation formula to calculate the corresponding comprehensive relationship metric value. The comprehensive relationship metric value calculation formula is:

[0116] S i =β*cosine_sim(X,P i )-α*D i ,

[0117] Wherein, α represents the position weight, β represents the feature weight, and D i Indicates the closeness corresponding to the i-th person, the cosine_sim(X, P i ) represents the position relationship measurement value corresponding to the i-th person.

[0118] Each module of the above-mentioned lost property recovery system can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules and units can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0119] like Figure 3 As shown, an embodiment of the present invention discloses a computer device, including a memory and a processor, wherein the memory stores a computer program;

[0120] The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the lost property recovery method described in the above embodiments is implemented.

[0121] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0122] An embodiment of the present invention further discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the lost property recovery method described in the above embodiments.

[0123] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0124] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A lost property recovery method, characterized in that: include: Perform feature extraction on the received lost property image to obtain a first feature vector; Acquire video data containing lost items and at least one person, and extract a second feature vector of each person in the video data; Calculate the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector; Acquire the position of the lost object and the positions of each person according to the video data, and calculate the position relationship measurement value between the lost object and the corresponding person according to the position of the lost object and the positions of each person; According to the closeness and position relationship measurement values ​​corresponding to each of the characters, the corresponding comprehensive relationship measurement value is calculated, and the person with the largest comprehensive relationship measurement value is taken as the owner.

2. The lost property recovery method according to claim 1, characterized in that: The calculating the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector includes: Substitute the first eigenvector and each second eigenvector into a compactness calculation formula to calculate the corresponding compactness. The compactness calculation formula is: Wherein, X represents the first eigenvector, and P i represents the second eigenvector, the ||X|| represents the norm of the first eigenvector, the ||P i || represents the norm of the second eigenvector.

3. The lost property recovery method according to claim 1, characterized in that: The calculating of the position relationship measurement value between the lost object and the corresponding person according to the lost object position and the positions of each person includes: Substitute the lost property location and the location of each person into the position relationship metric value calculation formula to calculate the corresponding position relationship metric value. The position relationship metric value calculation formula is: Among them, (x0, y0) represents the location of the lost item, Represents the position of the i-th person.

4. The lost property recovery method according to claim 1, characterized in that: The step of calculating the corresponding comprehensive relationship measurement value according to the closeness and position relationship measurement values ​​corresponding to each of the characters includes: Substitute each of the closeness and position relationship measurement values ​​into the comprehensive relationship measurement value calculation formula to calculate the corresponding comprehensive relationship measurement value, and the comprehensive relationship measurement value calculation formula is: S i =β*cosine_sim(X,P i )-α*D i , Wherein, α represents the position weight, β represents the feature weight, and D i Indicates the closeness corresponding to the i-th person, the cosine_sim(X, P i ) represents the position relationship measurement value corresponding to the i-th person.

5. The lost property recovery method according to any one of claims 1 to 4, characterized in that: The obtaining the location of the lost object and the locations of each person according to the video data includes: Identify the outline of the lost object and the outlines of each person according to the video data; Determine the lost property center according to the lost property outline, and use the lost property center as the lost property location; The corresponding character center is determined according to each character outline, and each character center is used as the corresponding character position.

6. The lost property recovery method according to any one of claims 1 to 4, characterized in that: The step of obtaining video data containing the lost object and at least one person includes: Obtaining the original video and the time when the lost property was picked up, wherein the original video is the video of the place where the lost property was picked up; Subtract the preset time period from the lost property picking up time to obtain the interception time; Starting from the interception time, video frames are selected in sequence forward along the time sequence, and lost object recognition is performed on the video frames until there is no lost object in the selected video frames, and the selection is stopped to obtain a first video clip containing the lost object; Perform person recognition on the video frames in the first video segment, select the video frames with people, obtain the second video segment, and acquire video data containing the lost property and at least one person.

7. The lost property recovery system is characterized by: include: A first extraction module, used for extracting features from the received lost property image to obtain a first feature vector; A second extraction module, used to obtain video data containing lost objects and at least one person, and extract a second feature vector of each person in the video data; A first calculation module, used for calculating the closeness between the lost object and the corresponding person according to the first feature vector and each second feature vector; A second calculation module is used to obtain the position of the lost object and the positions of each person according to the video data, and calculate the position relationship measurement value between the lost object and the corresponding person according to the position of the lost object and the positions of each person; The comprehensive calculation module is used to calculate the corresponding comprehensive relationship measurement value according to the closeness and position relationship measurement values ​​corresponding to each of the characters, and take the character with the largest comprehensive relationship measurement value as the owner.

8. The lost property recovery system according to claim 7, characterized in that: The first calculation module includes: A compactness calculation unit is used to substitute the first eigenvector and each second eigenvector into a compactness calculation formula to calculate the corresponding compactness, and the compactness calculation formula is: Wherein, X represents the first eigenvector, and P i represents the second eigenvector, the ||X|| represents the norm of the first eigenvector, the ||P i || represents the norm of the second eigenvector.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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