An object anti-lost method, device and storage medium

Abstract

This application provides a method, apparatus, and storage medium for preventing objects from being left behind, applied in the field of signal processing. The method includes: acquiring a target user and the distance from the target user to a vehicle; when the distance is less than a first preset threshold, acquiring the target user's identity and the identifier of the target object being carried; then, when a user is detected entering the vehicle, acquiring the user's identity and the identifier of the target object being carried; if the user's identity and the identifier of the target object are inconsistent with the target user's identity and the target object being carried, issuing a warning to the target user about lost objects. Thus, by comparing the consistency of the user's identity and the carried object inside and outside the vehicle, the user is reminded to check for lost objects, thereby preventing objects from being left outside the vehicle and falling during transit, improving the safety of the vehicle during operation.

Description

Technical Field

[0001] This application relates to the fields of signal processing and transmission as well as communication engineering, and in particular to a method, device and storage medium for preventing objects from being left behind. Background Technology

[0002] In recent years, the popularity of transportation such as cars and subways has gradually increased, with more and more people choosing to travel by public transport. Consequently, road safety has become a major concern. Sometimes, users temporarily place items they are carrying outside the vehicle before entering, but then forget to take them inside. For example, they might place a water bottle, phone, or wallet on the roof of a car and forget to take them inside, leaving them behind. If the vehicle is in motion, these items can easily fall, creating a road safety hazard. Furthermore, falling objects can cause property damage to their owners.

[0003] Therefore, how to prevent users from leaving their belongings outside the vehicle and causing them to fall during travel, thus improving road safety, has become an urgent technical problem to be solved. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method, device, and storage medium for preventing objects from being left behind, aiming to prevent users from leaving objects outside the vehicle and causing them to fall off during driving, thereby improving road safety.

[0005] In a first aspect, embodiments of this application provide a method for preventing objects from being dropped, the method comprising:

[0006] Acquire target users, which are users whose distance from transportation is within a preset range;

[0007] Obtain the distance from the target user to their mode of transportation;

[0008] In response to the target user's distance from the vehicle being less than a first preset threshold, the system obtains the target user's identity and the identifier of the target object being carried.

[0009] In response to a user entering the vehicle, the system obtains the user's identification and the identification of the object being carried.

[0010] In response to the fact that the identity identifier of the user to be detected is the same as the identity identifier of the target user, it is determined whether the identifier of the object to be detected carried is consistent with the identifier of the target object carried;

[0011] In response to the discrepancy between the carried object identifier and the carried target object identifier, an object loss alarm is triggered for the target user.

[0012] Optionally, obtaining the target user's identity and the target object's identifier includes:

[0013] Acquire image data of the target user, wherein the image data of the target user is acquired through an acquisition device;

[0014] Based on the first preset target detection algorithm and the image data of the target user, obtain the identity identifier of the first user and the identifier of the target object carried by the target user;

[0015] The first preset target detection algorithm is used to identify target users in image data and detect the identity of the target users and the objects they are carrying.

[0016] Optionally, obtaining the identity identifier of the user to be detected and the identifier of the object to be detected carried includes:

[0017] The image data of the user to be detected is acquired by the acquisition device inside the vehicle.

[0018] Based on the second preset target detection algorithm and the image data of the user to be detected, obtain the identity identifier of the user to be detected and the identifier of the object to be detected carried by the user.

[0019] The second preset target detection algorithm is used to identify the user to be detected in the image data and to detect the identity of the user and the objects they are carrying.

[0020] Optionally, determining whether the carried object identifier to be detected is consistent with the carried target object identifier includes:

[0021] Based on a preset object consistency recognition algorithm, it is determined whether the carried object identifier to be detected is consistent with the carried target object identifier.

[0022] Optionally, in response to the user to be detected entering the vehicle, the method further includes:

[0023] In response to the target user's distance from the vehicle being less than a second preset threshold, the dwell time of the target user around the vehicle is obtained;

[0024] In response to the dwell time being lower than a preset dwell time threshold, it is determined whether the user to be detected has entered the vehicle.

[0025] Optionally, the method further includes:

[0026] In response to the dwell time exceeding a preset dwell time threshold, an object loss alarm is triggered for the target user.

[0027] Secondly, embodiments of this application provide an object-prevention device, the device comprising:

[0028] The first acquisition unit is used to acquire target users, wherein the target users are users whose distance from traffic is within a preset range;

[0029] The second acquisition unit is used to acquire the distance from the target user to the means of transportation;

[0030] The first response unit is used to obtain the identity identifier of the target user and the identifier of the target object being carried in response to the distance between the target user and the vehicle being less than a first preset threshold.

[0031] The second response unit is used to respond to the user to be detected entering the vehicle and to obtain the identity of the user to be detected and the identification of the object to be detected being carried.

[0032] The third response unit is used to determine whether the carried object identifier is consistent with the carried target object identifier in response to the fact that the identity identifier of the user to be detected is the same as the identity identifier of the target user.

[0033] The fourth response unit is used to perform an object loss alarm operation on the target user in response to the inconsistency between the carried object identifier and the carried target object identifier.

[0034] Optionally, the first response unit includes:

[0035] The first acquisition module is used to acquire image data of the target user, wherein the image data of the target user is acquired through an acquisition device.

[0036] The first detection module is used to obtain the identity identifier of the first user and the identifier of the target object carried by the first user based on the first preset target detection algorithm and the image data of the target user.

[0037] The first preset target detection algorithm is used to identify target users in image data and detect the identity of the target users and the objects they are carrying.

[0038] Optionally, the second response unit includes:

[0039] The second acquisition module is used to acquire the image data of the user to be detected, which is acquired by acquisition equipment inside the vehicle.

[0040] The second detection module is used to obtain the identity identifier of the user to be detected and the identifier of the object to be detected carried by the user based on the second preset target detection algorithm and the image data of the user to be detected.

[0041] The second preset target detection algorithm is used to identify the user to be detected in the image data and to detect the identity of the user and the objects they are carrying.

[0042] Thirdly, embodiments of this application provide a computer storage medium storing code, wherein when the code is executed, a device running the code implements the method described in any of the first aspects above.

[0043] This application provides a method, apparatus, and storage medium for preventing objects from being left behind. When executing the method, firstly, the target user and the distance from the target user to the vehicle are obtained. When this distance is less than a first preset threshold, the target user's identity and the identifier of the target object being carried are obtained. Then, when a user to be detected enters the vehicle, the user's identity and the identifier of the target object being carried are obtained. If the user's identity and the identifier of the target object being carried do not match the target user's identity and the identifier of the target object, an object loss alarm is triggered for the target user. Thus, by comparing the consistency of the user's identity and the object being carried inside and outside the vehicle, the user is reminded to perform object loss detection, thereby preventing the user from leaving objects outside the vehicle and causing safety hazards due to objects falling during transit, thus improving the safety of the vehicle during operation. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart of a method for preventing objects from falling, provided in an embodiment of this application;

[0046] Figure 2 A flowchart illustrating a method for obtaining a user's identity and carried objects, provided in an embodiment of this application;

[0047] Figure 3 A flowchart illustrating another method for obtaining a user's identity and carried objects, provided in an embodiment of this application;

[0048] Figure 4 A flowchart illustrating another method for preventing objects from falling, provided in an embodiment of this application;

[0049] Figure 5 This is a schematic diagram of an object-prevention device provided in an embodiment of this application. Detailed Implementation

[0050] As mentioned earlier, users sometimes leave items such as water bottles, mobile phones, and wallets outside the vehicle before entering, only to forget to bring them inside. This can cause these items to fall while the vehicle is in motion, creating a road safety hazard and resulting in property damage.

[0051] Based on this, this application proposes to identify the consistency of objects carried by users inside and outside the vehicle when entering traffic. If the objects carried by the user inside and outside the vehicle are inconsistent, it indicates that the user may have left an object outside the vehicle, thus reminding the user to perform an object loss detection operation. In this way, the safety hazards caused by users leaving objects outside the vehicle during operation are avoided, thereby improving the safety of the vehicle during operation.

[0052] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0053] See Figure 1 This is a flowchart illustrating a method for preventing objects from being left behind, provided in an embodiment of this application. It is applied to a vehicle handling system. The method includes at least the following steps:

[0054] S101: Obtain target users.

[0055] In this embodiment, to prevent objects from being lost, it is first necessary to acquire the target user. In this embodiment, the target user is a user within a preset range of the vehicle. In this embodiment, the target user can be acquired in various ways. For example, it can be determined whether the user is within the preset range of the vehicle handling system; if so, that user is the target user required by this application. Alternatively, it can be determined by identifying an object within the preset range of the vehicle, thus identifying the user carrying that object; that user is the target user required by this application. Those skilled in the art will understand that the preset range can be set as needed.

[0056] S102: Obtain the distance from the target user to their mode of transportation.

[0057] In this embodiment, after acquiring the target user, the distance from the target user to the vehicle is further acquired. Only when the distance meets certain conditions is the object-prevention operation performed on the user. In this embodiment, the distance from the target user to the vehicle can be acquired in various ways, such as by using a distance sensor to collect the distance in real time.

[0058] S103: In response to the distance between the target user and the vehicle being less than a first preset threshold, obtain the target user's identity and the identification of the target object being carried.

[0059] After obtaining the distance from the target user to the vehicle, if the distance is less than a first preset threshold, the target user's identity identifier and the identifier of the target object being carried are obtained. In this embodiment, the identifier of the target object being carried is a tag corresponding to the content attributes of a specific object carried by the target user at a preset body position. The preset body position is a body part pre-set by the vehicle processing system, such as a hand. Content attributes include object category and object color. For example, assuming the preset body position is a hand, the tag "blue flowered water cup" corresponding to a blue flowered water cup carried by the target user is the identifier of the target object being carried.

[0060] In this embodiment, a built-in judgment algorithm can be incorporated into the transportation handling system to determine whether the distance between the target user and the transportation vehicle is less than a first preset threshold. Those skilled in the art will understand that the first preset threshold can be adjusted as needed. In this embodiment, by using distance judgment, the transportation handling system only initiates the object-avoidance method process when the user approaches the transportation vehicle. This avoids operating on all users and reduces the workload of the object-avoidance method.

[0061] In this embodiment, a first preset target detection algorithm can be pre-built into the transportation processing system. By acquiring the target user's image data and using the first preset target detection algorithm, the target user's identity and the identification of the target object being carried can be obtained. In this embodiment, the target user's image data can be acquired through a data acquisition device, such as a vehicle's built-in camera. The first preset target detection algorithm is used to identify the target user in the image data and detect the target user's identity and the object being carried. In this embodiment, the first preset target detection algorithm can be a YOLO detection algorithm, Faster R-CNN detection algorithm, or other algorithms capable of target detection. In this embodiment, the identity identifier is used to prove the user's identity, such as the user's facial features.

[0062] In this embodiment of the application, if no target object carried by the target user is detected, subsequent operations are stopped.

[0063] S104: In response to the user to be detected entering the vehicle, obtain the identity of the user to be detected and the identification of the object to be detected being carried.

[0064] After the vehicle processing system obtains the identity identifier of the target user and the identifier of the target object they are carrying, it determines whether the user to be detected has entered the vehicle. In this embodiment, a camera inside the vehicle can be used to detect whether anyone has entered the vehicle. Alternatively, the determination can be made by checking if someone has entered the vehicle. In this embodiment, the user to be detected specifically refers to a user who has entered the vehicle. When it is detected that a user to be detected has entered the vehicle, it is necessary to obtain the identity identifier of the user to be detected and the identifier of the target object they are carrying.

[0065] In this embodiment, the identity of the user to be detected and the identification of any objects carried by the user can be obtained through a second preset target detection algorithm and the acquired image data of the user to be detected. In this embodiment, the second preset target detection algorithm is used to identify the user to be detected in the image data and detect the user's identity and the objects carried by the user. In this embodiment, the second preset target detection algorithm can be an algorithm capable of target detection, such as the YOLO detection algorithm or the Faster R-CNN detection algorithm. The image data of the target user to be detected is acquired through acquisition equipment inside the vehicle.

[0066] In this embodiment, the object to be detected is identified by a tag corresponding to the content attributes of the specific object carried by the user. Content attributes include object category and object color. For example, the tag "blue flowery water cup" corresponding to the blue flowery water cup carried by the user is the target object identifier.

[0067] S105: In response to the fact that the identity identifier of the user to be detected is the same as the identity identifier of the target user, determine whether the identifier of the object to be detected carried is consistent with the identifier of the target object carried.

[0068] In this embodiment, after obtaining the identity identifier of the user to be detected and the identifier of the object being carried, the transportation processing system needs to determine whether the identity identifier of the user to be detected is the same as that of the target user. If they are the same, it further determines whether the identifier of the object being detected is consistent with the identifier of the target object being carried. Since the user to be detected and the target user are identified as the same user, if it is detected that the user to be detected is not carrying an object, an object loss alarm can be directly triggered for the target user. If the identity identifier of the user to be detected is different from that of the target user, no further operations are performed.

[0069] In this embodiment, a pre-set object consistency recognition algorithm can be used in the transportation processing system to perform consistency detection between the identifier of the object to be detected and the identifier of the carried target object. In this embodiment, the object consistency recognition algorithm can be obtained by training a deep neural network based on softmax. Specifically, firstly, the identifier of the object to be detected and the identifier of the carried target object are obtained based on the trained softmax deep neural network. Then, the two object identifiers are compared to see if they are the same. If they are the same, it means that the two object identifiers are consistent; otherwise, the two object identifiers are inconsistent.

[0070] In this embodiment, the object consistency recognition algorithm can also be obtained by image similarity algorithm. For example, the image similarity between the target object corresponding to the carried target object identifier and the object to be detected corresponding to the carried object to be detected identifier can be calculated using a cosine similarity algorithm. If the similarity exceeds a preset similarity threshold, it indicates that the two object identifiers are consistent; otherwise, the two object identifiers are inconsistent.

[0071] In this embodiment of the application, the object consistency algorithm can also be other algorithms that can determine whether the target object identifier carried and the object identifier to be detected carried are consistent.

[0072] S106: In response to the discrepancy between the carried object identifier and the carried target object identifier, an object loss alarm is triggered for the target user.

[0073] In this embodiment, when the identifier of the object to be detected carried is inconsistent with the identifier of the target object carried, an object loss alarm is triggered for the target user. For example, this can be achieved by issuing a voice prompt or a lost object alarm. Specifically, in this embodiment, the inconsistency between the identifier of the object to be detected and the identifier of the target object includes: the identifiers are different, for example, the identifier of the object to be detected is a blue water cup with flowers, while the identifier of the target object is a yellow water cup with flowers; or the identifiers are the same, but the image similarity is low, for example, both the identifier of the object to be detected and the identifier of the target object are blue water cups with flowers, but the images of the two are not very similar.

[0074] In this embodiment of the application, when the identification of the object to be detected carried is consistent with the identification of the target object carried, it means that the target user has not left anything outside the vehicle and no reminder operation is performed.

[0075] This application provides a method for preventing objects from being left behind. First, it obtains the target user and the distance from the target user to the vehicle. When this distance is less than a first preset threshold, it obtains the target user's identity and the identifier of the target object they are carrying. Then, when a user is detected entering the vehicle, it obtains the user's identity and the identifier of the object they are carrying. If the user's identity and the identifier of the object they are carrying do not match those of the target user, an object loss alarm is triggered for the target user. Thus, by comparing the consistency of the user's identity and the object they are carrying inside and outside the vehicle, the user is reminded to perform object loss detection, thereby preventing the user from leaving objects outside the vehicle and causing safety hazards such as objects falling during transit, thus improving the safety of the vehicle during operation.

[0076] There are multiple ways to obtain the identity identifier of the target user and the identifier of the target object carried in steps S102 and S103, and to obtain the identity identifier of the user to be detected and the identifier of the object to be detected carried. These will be described in detail below.

[0077] See Figure 2 This is a flowchart illustrating a method for obtaining a user's identity and the object they are carrying, provided in an embodiment of this application. The method includes at least the following steps:

[0078] S201: Call the camera to collect image data of the target user.

[0079] S202: Based on the preset detection algorithm A and the image data of the target user, obtain the identity of the target user and the identification of the target object they are carrying.

[0080] S203: When the user to be detected enters the vehicle, collect the image data of the user to be detected.

[0081] S204: Based on the preset detection algorithm A and the image data of the data to be detected, obtain the identity of the user to be detected and the identification of the object to be detected carried.

[0082] In the above embodiments, the preset detection algorithm A can be the YOLO detection algorithm, the Faster R-CNN detection algorithm, etc.

[0083] See Figure 3 This is a flowchart illustrating another method for obtaining a user's identity and a carried object, provided in an embodiment of this application. The method includes at least the following steps:

[0084] S301: Call the camera to collect image data of the target user.

[0085] S202: Based on the preset detection algorithm A and the image data of the target user, obtain the identity of the target user and the identification of the target object they are carrying.

[0086] S203: When the user to be detected enters the vehicle, collect the image data of the user to be detected.

[0087] S204: Based on the preset detection algorithm B and the image data of the data to be detected, obtain the identity of the user to be detected and the identification of the object to be detected carried.

[0088] In the embodiments of this application, the prediction detection algorithm A and B are different preset detection algorithms, such as A being the YOLO detection algorithm and B being the Faster R-CNN detection algorithm, or A being the Faster R-CNN detection algorithm and B being the YOLO detection algorithm, etc.

[0089] Furthermore, to further improve the accuracy and speed of object drop prevention detection, this application also provides a method for object drop prevention that takes into account dwell time. See [link to application]. Figure 4 This is a flowchart illustrating another method for preventing objects from being dropped, provided in an embodiment of this application. The method includes at least the following steps:

[0090] S401: Uses a distance sensor to obtain the distance S from the target user to the vehicle in real time.

[0091] S402: Determine whether the distance S is less than the first preset threshold. If yes, proceed to S403; otherwise, proceed to S410.

[0092] S403: Obtain the target user's identity (IDA) and the object (PA) carried by the target user according to the preset target detection algorithm A.

[0093] S404: In response to the distance S being less than the second preset threshold, obtain the dwell time of the target user around the vehicle.

[0094] In this embodiment, the second preset threshold is less than the first preset threshold. For example, the first preset threshold is 3m, meaning that the anti-drop operation is initiated when the target user is 3m away from the vehicle. The second preset threshold is 2m, meaning that the time the target user spends around the vehicle is only counted when the target user is 2m away from the vehicle. In this embodiment, the first and second preset thresholds can be adjusted as needed.

[0095] S405: Determine if the dwell time exceeds the preset dwell time threshold. If no, proceed to S406. If yes, proceed to S409.

[0096] Example Explanation: Assume the preset dwell time threshold is 10 seconds. That is, when the dwell time is 6 seconds, it indicates the dwell time is below the threshold, and operation S406 is initiated. If the dwell time is 11 seconds, exceeding the preset dwell time threshold, operation S409 is initiated. Those skilled in the art will understand that the preset dwell time threshold can be adjusted as needed.

[0097] S406: Determine whether the user to be detected has entered the vehicle. If yes, proceed to S407. Otherwise, proceed to S410.

[0098] S407: Obtain the identity IDB of the user to be detected and the object PB carried by the user according to the preset target detection algorithm B.

[0099] S408: When IDB and IDA are the same, determine whether PA and PB are consistent using the consistency identification algorithm. If yes, proceed to S410; otherwise, proceed to S409.

[0100] S409: Perform an object loss alarm operation on the target user.

[0101] S410: No action.

[0102] In this embodiment of the application, "no action" means not performing any object loss alarm operation on the target user.

[0103] In the embodiments provided in this application, the duration of continuous stay is obtained when the distance between the target user and the vehicle is lower than a second preset threshold. The probability that the target user will place an object outside the vehicle is determined based on the stay time. This not only avoids the safety hazard of users leaving objects outside the vehicle and causing objects to fall during transit, thus improving the safety of the vehicle during operation, but also further enhances the accuracy and speed of object detection to prevent objects from being left behind.

[0104] In addition, this application also provides a corresponding device for preventing objects from falling. See [link to application]. Figure 5 This is a schematic diagram of the structure of an object-prevention device 500 provided in an embodiment of this application. As shown in the figure, the device 500 includes at least the following units:

[0105] The first acquisition unit 501 is used to acquire a target user, wherein the target user is a user whose distance from the traffic is within a preset range;

[0106] The second acquisition unit 502 is used to acquire the distance from the target user to the means of transportation;

[0107] The first response unit 503 is used to obtain the identity identifier of the target user and the identifier of the target object being carried in response to the distance from the target user to the vehicle being less than a first preset threshold.

[0108] The second response unit 504 is used to respond to the user to be detected entering the vehicle and to obtain the identity of the user to be detected and the identification of the object to be detected carried.

[0109] The third response unit 505 is used to determine whether the carried object identifier is consistent with the carried target object identifier in response to the fact that the identity identifier of the user to be detected is the same as the identity identifier of the target user.

[0110] The fourth response unit 506 is used to perform an object loss alarm operation on the target user in response to the inconsistency between the carried object identifier and the carried target object identifier.

[0111] Optionally, the first response unit 503 includes:

[0112] The first acquisition module is used to acquire image data of the target user, wherein the image data of the target user is acquired through an acquisition device.

[0113] The first detection module is used to obtain the identity identifier of the first user and the identifier of the target object carried by the first user based on the first preset target detection algorithm and the image data of the target user.

[0114] The first preset target detection algorithm is used to identify target users in image data and detect the identity of the target users and the objects they are carrying.

[0115] Optionally, the second response unit 504 includes:

[0116] The second acquisition module is used to acquire the image data of the user to be detected, which is acquired by acquisition equipment inside the vehicle.

[0117] The second detection module is used to obtain the identity identifier of the user to be detected and the identifier of the object to be detected carried by the user based on the second preset target detection algorithm and the image data of the user to be detected.

[0118] The second preset target detection algorithm is used to identify the user to be detected in the image data and to detect the identity of the user and the objects they are carrying.

[0119] Optionally, the third response unit 505 is further configured to determine whether the carried object identifier to be detected is consistent with the carried target object identifier according to a preset object consistency recognition algorithm.

[0120] Optionally, device 500 also includes:

[0121] The fifth response unit is used to obtain the dwell time of the target user around the vehicle in response to the distance from the target user to the vehicle being less than a second preset threshold.

[0122] The sixth response unit is used to determine whether the user to be detected has entered the vehicle in response to the dwell time being lower than a preset dwell time threshold.

[0123] Optionally, the sixth response unit is also configured to perform an object loss alarm operation on the target user in response to the dwell time exceeding a preset dwell time threshold.

[0124] This application provides an object-prevention device. A first acquisition unit 501 acquires the identity of a user within a preset range of a vehicle. An acquisition unit 502 acquires the distance from the target user to the vehicle. A first response unit 503 acquires the identity of the target user and the identifier of the target object being carried when the distance is less than a first preset threshold. A second response unit 504 acquires the identity of the user to be detected and the identifier of the target object being carried when a user is detected entering the vehicle. A third response unit 505, in response to the user's identity and the identifier of the target object being carried matching the identity of the target user and the identifier of the target object, issues an object-prevention alarm to the target user. Thus, by detecting and comparing the user's identity and the object being carried in the internal and external environments of the vehicle, the device reminds the user to detect the object being left behind, thereby preventing the user from leaving objects outside the vehicle and causing safety hazards due to objects falling during travel, thus improving the safety of the vehicle during travel.

[0125] This application also provides corresponding devices and computer-readable storage media for implementing the solutions provided in this application.

[0126] The device includes a memory and a processor. The memory is used to store instructions or code, and the processor is used to execute the instructions or code to cause the device to perform an object-prevention method according to any embodiment of this application.

[0127] In practical applications, the computer-readable storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0128] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0129] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0130] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0131] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0132] The above description is merely one specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for preventing objects from being lost, characterized in that, The method includes: Acquire target users, which are users within a preset range of the vehicle; Obtain the distance from the target user to their mode of transportation; In response to the target user's distance from the vehicle being less than a first preset threshold, the system obtains the target user's identity and the identifier of the target object being carried. In response to a user entering the vehicle, the system obtains the user's identification and the identification of the object being carried. In response to the fact that the identity identifier of the user to be detected is the same as the identity identifier of the target user, a preset object consistency recognition algorithm is used to determine whether the carried object identifier to be detected is consistent with the carried target object identifier; the object consistency recognition algorithm is obtained by training a deep neural network based on softmax, or by calculating through a cosine similarity algorithm; In response to the discrepancy between the carried object identifier and the carried target object identifier, an object loss alarm is triggered for the target user.

2. The method according to claim 1, characterized in that, The acquisition of the target user's identity and the target object's identifier includes: Acquire image data of the target user, wherein the image data of the target user is acquired through an acquisition device; Based on the first preset target detection algorithm and the image data of the target user, obtain the identity identifier of the first user and the identifier of the target object carried by the target user; The first preset target detection algorithm is used to identify target users in image data and detect the identity of the target users and the objects they are carrying.

3. The method according to claim 1, characterized in that, The process of obtaining the identity identifier of the user to be detected and the identifier of the object to be detected carried includes: The image data of the user to be detected is acquired by the acquisition device inside the vehicle. Based on the second preset target detection algorithm and the image data of the user to be detected, obtain the identity identifier of the user to be detected and the identifier of the object to be detected carried by the user. The second preset target detection algorithm is used to identify the user to be detected in the image data and to detect the identity of the user and the objects they are carrying.

4. The method according to any one of claims 1-3, characterized in that, In response to the user to be detected entering the vehicle, the method further includes: In response to the target user's distance from the vehicle being less than a second preset threshold, the dwell time of the target user around the vehicle is obtained; In response to the dwell time being lower than a preset dwell time threshold, it is determined whether the user to be detected has entered the vehicle.

5. The method according to claim 4, characterized in that, The method further includes: In response to the dwell time exceeding a preset dwell time threshold, an object loss alarm is triggered for the target user.

6. A device for preventing objects from falling, characterized in that, The device includes: The first acquisition unit is used for target users, wherein the target users are users whose distance from traffic is within a preset range; The second acquisition unit is used to acquire the distance from the target user to the means of transportation; The first response unit is used to obtain the identity identifier of the target user and the identifier of the target object being carried in response to the distance between the target user and the vehicle being less than a first preset threshold. The second response unit is used to respond to the user to be detected entering the vehicle and to obtain the identity of the user to be detected and the identification of the object to be detected being carried. The third response unit is used to respond to the fact that the identity identifier of the user to be detected is the same as the identity identifier of the target user, and to determine whether the carried object identifier is consistent with the carried target object identifier according to a preset object consistency recognition algorithm; the object consistency recognition algorithm is obtained by training a deep neural network based on softmax, or by calculating through a cosine similarity algorithm; The fourth response unit is used to perform an object loss alarm operation on the target user in response to the inconsistency between the carried object identifier and the carried target object identifier.

7. The apparatus according to claim 6, characterized in that, The first response unit includes: The first acquisition module is used to acquire image data of the target user, wherein the image data of the target user is acquired through an acquisition device. The first detection module is used to obtain the identity identifier of the first user and the identifier of the target object carried by the first user based on the first preset target detection algorithm and the image data of the target user. The first preset target detection algorithm is used to identify target users in image data and detect the identity of the target users and the objects they are carrying.

8. The apparatus according to claim 6, characterized in that, The second response unit includes: The second acquisition module is used to acquire the image data of the user to be detected, which is acquired by acquisition equipment inside the vehicle. The second detection module is used to obtain the identity identifier of the user to be detected and the identifier of the object to be detected carried by the user based on the second preset target detection algorithm and the image data of the user to be detected. The second preset target detection algorithm is used to identify the user to be detected in the image data and to detect the identity of the user and the objects they are carrying.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an implementation program of a method for preventing objects from being left behind, which, when executed by a processor, implements the steps of the method as described in any one of claims 1-5.

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