Scenic spot lost and found method and device, computer equipment, storage medium and computer program product

Abstract

The application provides a scenic spot lost-and-found method and device, computer equipment, storage medium and computer program product. The method comprises the following steps: acquiring an item feature corresponding to a lost item and lost space-time information; performing lost item matching in a lost-and-found database based on the item feature; if the lost item is not matched, determining at least one to-be-searched monitoring video in a monitoring system of the scenic spot based on the lost space-time information; determining a tracking target in each to-be-searched monitoring video based on the item feature, performing cross-lens track tracking on the tracking target, and generating a moving track; and generating a lost-and-found prompt information based on the moving track and pushing the lost-and-found prompt information to a terminal of a lost owner. The method can improve the recovery efficiency of lost items in the scenic spot.

Description

Technical Field

[0001] This application relates to the field of smart scenic area management technology, and in particular to a method, device, computer equipment, storage medium and computer program product for recovering lost items in scenic areas. Background Technology

[0002] Scenic spots usually set up lost and found offices as fixed places for collecting and claiming items.

[0003] Currently, tourists can register lost items at the lost and found office within the scenic area. The tourist or staff who finds the item then hand it over to the office for claiming. However, this method relies heavily on traditional manual processing. Whether lost items can be recovered depends on the finder's initiative to hand them in and the owner's initiative to inquire, resulting in low efficiency. Furthermore, tourists cannot know if their items have been moved, and even if they know the exact location where they were lost, it is still difficult to retrieve them.

[0004] Therefore, traditional technologies have the problem of not being able to efficiently retrieve lost items when tourists lose them in scenic areas. Summary of the Invention

[0005] Based on this, the purpose of this application is to at least solve one of the above-mentioned technical defects, especially the technical defect in the prior art that tourists cannot efficiently retrieve lost items in scenic areas. This application provides a method, device, computer equipment, computer-readable storage medium and computer program product that can improve the efficiency of retrieving lost items in scenic areas.

[0006] Firstly, this application provides a method for recovering lost items in scenic areas, the method including:

[0007] Obtain the item characteristics and spatiotemporal information of the lost item;

[0008] Based on the characteristics of the items, the lost items are matched in the lost and found database. If no lost items are matched, at least one surveillance video to be retrieved is determined from the scenic area's monitoring system based on the time and space information of the loss.

[0009] Based on the characteristics of the items, the tracking target is identified in each surveillance video to be retrieved, and cross-camera trajectory tracking is performed on the tracking target to generate a movement trajectory;

[0010] Based on the movement trajectory, a lost item retrieval notification is generated and pushed to the owner's terminal.

[0011] In an exemplary embodiment, the lost spatiotemporal information includes the lost location and the lost time. Based on the lost spatiotemporal information, at least one surveillance video to be retrieved is identified in the scenic area's monitoring system, including:

[0012] Based on the location of loss, determine the spatial search range; and based on the time of loss, determine the temporal search range.

[0013] The monitoring system in the scenic area filters out the surveillance cameras within the spatial search range, and extracts video segments within the temporal search range from the corresponding surveillance videos of the surveillance cameras as the surveillance videos to be searched.

[0014] In one exemplary embodiment, determining the tracking target in each surveillance video to be retrieved based on item characteristics includes:

[0015] Identify at least one item in each surveillance video to be retrieved, and extract the item features of each item;

[0016] Based on the characteristics of each item and the characteristics of the lost item, determine the similarity of characteristics between each item and the lost item;

[0017] Based on the similarity of various features, the tracking target is determined from each item.

[0018] In one exemplary embodiment, determining the tracking target from each item based on feature similarity includes:

[0019] Items with feature similarity greater than a preset similarity threshold are identified as suspected lost items. Based on each suspected lost item, a lost item matching list is generated and pushed to the owner's terminal.

[0020] In response to the owner's selection of items from the list of lost items, the suspected lost item selected in the item selection operation is identified as the tracking target.

[0021] In one exemplary embodiment, a missing item matching list is generated based on each suspected missing item, including:

[0022] Determine the correlation between the time of loss and the location of loss between each suspected lost item and the actual lost item;

[0023] Based on the similarity of features, correlation of loss time, and correlation of loss location between each suspected lost item and the lost item, the matching degree between each suspected lost item and the lost item is determined.

[0024] Sort the suspected lost items according to the matching degree from high to low, and obtain the lost item matching list.

[0025] In one exemplary embodiment, cross-camera trajectory tracking is performed on the target to generate a motion trajectory, including:

[0026] Identify at least one target surveillance camera where the tracked target appears, and determine the first appearance time and final disappearance time of the tracked target at each target surveillance camera;

[0027] Acquire video streams for each target surveillance camera from its first appearance time to its final disappearance time. Perform single-target tracking on the tracked target in each video stream to generate a local tracking trajectory for each target surveillance camera and extract the appearance features of the tracked target in each local tracking trajectory.

[0028] Based on the similarity between various appearance features and the temporal connection between various local tracking trajectories, cross-camera association is performed on various local tracking trajectories to generate motion trajectories.

[0029] In one exemplary embodiment, the method further includes:

[0030] During the cross-camera trajectory tracking process, when the tracked target is detected to be picked up, facial features of the picker are extracted to obtain the picker's facial features.

[0031] The facial features of the finder are compared with the facial database of the scenic area. If the comparison is successful, the contact information of the finder is obtained through the facial database of the scenic area.

[0032] Based on the contact information, the system pushes item delivery guidance information to the finder's device.

[0033] Secondly, this application provides a lost and found device for scenic areas, the device comprising:

[0034] The acquisition module is used to acquire the item characteristics and spatiotemporal information of the lost item.

[0035] The matching module is used to match lost items in the lost and found database based on the characteristics of the items. If no lost item is matched, at least one surveillance video to be retrieved is determined from the scenic area's monitoring system based on the spatiotemporal information of the time of loss.

[0036] The tracking module is used to identify the tracking target in each surveillance video to be retrieved based on the characteristics of the object, and to perform cross-camera trajectory tracking on the tracking target to generate a movement trajectory;

[0037] The push module is used to generate lost item retrieval notifications based on the movement trajectory and push them to the owner's terminal.

[0038] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0039] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0040] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0041] The lost and found method, device, computer equipment, storage medium, and computer program product provided in this application acquire the characteristics of the lost item and its spatiotemporal information. Based on the item characteristics, a lost item is matched in a lost and found database. If no matching is found, at least one surveillance video to be retrieved is identified in the scenic area's monitoring system based on the spatiotemporal information. Based on the item characteristics, a tracking target is identified in each of the surveillance videos to be retrieved, and cross-camera trajectory tracking is performed on the tracking target to generate a movement trajectory. Based on the movement trajectory, a lost and found prompt message is generated and pushed to the owner's terminal. In this way, image recognition and cross-camera trajectory tracking technology are combined to construct an intelligent lost and found process. After an item is lost, the target can be quickly located from massive amounts of monitoring data and its movement path can be reconstructed, providing the owner with a clear direction for searching. This improves the efficiency and possibility of retrieving lost items in complex scenic environments and solves the problem of low retrieval efficiency in traditional technologies that passively wait for finders to hand over the items or manually check the monitoring data. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments 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.

[0043] Figure 1 This application environment diagram illustrates a method for retrieving lost items in scenic areas, as provided in this embodiment.

[0044] Figure 2 This is a schematic diagram of an intelligent matching process for lost items in one embodiment;

[0045] Figure 3 This is a schematic diagram of an item tracking process in one embodiment;

[0046] Figure 4 This is a schematic diagram of the smart claim verification mechanism in one embodiment;

[0047] Figure 5 This is a flowchart illustrating a method for recovering lost items in a scenic area, as described in another embodiment.

[0048] Figure 6 This is a structural block diagram of a lost and found device in a scenic area, as shown in one embodiment.

[0049] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] In one exemplary embodiment, such as Figure 1 As shown, a method for retrieving lost items in a scenic area is provided. This method is applied to a server for illustration and includes the following steps S102 to S108. Wherein:

[0052] Step S102: Obtain the item characteristics and spatiotemporal information of the lost item.

[0053] Among them, item characteristics refer to those extracted based on the lost item information reported by the owner on the tourist app. Lost item information may include uploaded photos of the lost item (3-5 photos from multiple angles), item description (item category, such as mobile phone, wallet, backpack, or camera), feature annotations (such as color, brand, model, or special markings), location of loss (approximate location marked on an electronic map), time of loss (selected or estimated time period of loss), contact information (such as encrypted mobile phone number, WeChat, etc.), and urgency level (such as general, important, or urgent).

[0054] In practical applications, when extracting item features, AI is used to automatically extract item features based on lost item information. These features include, for example, item type features identified through image classification, item color features (dominant color, secondary color), item shape features (aspect ratio, outline), item texture features (material, pattern), and text recognition results (brand logo, label text). Then, an item feature vector is generated based on the extracted features for subsequent matching.

[0055] In practical applications, the loss scenario is also inferred based on lost item information. Specifically, by combining the tourist's travel trajectory (such as the tourist's APP location record), the location of the last used item is analyzed to infer the high-probability loss area and list 3-5 possible loss locations, and the monitoring of these areas is retrieved first.

[0056] Among them, the spatiotemporal information of loss refers to the approximate geographical location and approximate time (point in time or time period) of the lost item reported by the owner.

[0057] Optionally, the server receives information about lost items uploaded by the owner via the terminal, and extracts the item's characteristics and the time and space information of its loss based on this information.

[0058] Step S104: Based on the characteristics of the item, perform a lost item matching in the lost and found database. If no lost item is matched, then based on the spatiotemporal information of the loss, determine at least one surveillance video to be retrieved in the scenic area's monitoring system.

[0059] The lost and found database is a database that stores information on lost items collected within the scenic area. Each record typically includes an image of the collected item, a description of its features, the location where it was found, and the time it was found.

[0060] Among them, the surveillance videos to be retrieved refer to video clips that may record traces of lost items, which are initially selected from the massive amount of surveillance videos in the scenic area's monitoring system based on the spatiotemporal information of the time of loss.

[0061] Optionally, the server first compares the lost item with the lost and found database based on the item's characteristics. If no match is found, the tracking process begins, which involves locating surveillance cameras that may have recorded traces of the lost item based on the time and space information of the loss, and retrieving the video from the corresponding time period as the surveillance video to be searched.

[0062] In practical applications, when searching the lost and found database, the system first checks if anyone has already brought in a similar item. If someone has already brought in a similar item, the system compares the similarity of the features. If the similarity is greater than 80%, the system automatically pushes the matching result, and the owner can claim the item directly after confirmation.

[0063] Step S106: Based on the characteristics of the object, determine the tracking target in each surveillance video to be retrieved, and perform cross-camera trajectory tracking on the tracking target to generate a movement trajectory.

[0064] The tracking target refers to a visual target in the surveillance video that is identified as a possible lost item.

[0065] Cross-camera trajectory tracking refers to the process of continuously and automatically identifying and associating the same tracking target in the video streams of different cameras in a network of multiple surveillance cameras, thereby reconstructing its movement path throughout the entire monitoring area.

[0066] Among them, the movement trajectory is a path information generated after cross-camera tracking, which describes the continuous movement of the tracked target in time and space.

[0067] Optionally, the server uses target detection and recognition algorithms to find objects in these surveillance videos that match the characteristics of the lost items and marks them as tracking targets. Then, it activates a multi-camera collaborative trajectory tracking algorithm to identify the movement process of the tracking target and finally generate its movement trajectory.

[0068] Step S108: Based on the movement trajectory, generate a lost item retrieval notification message and push it to the owner's terminal.

[0069] Among them, the lost item retrieval prompts are text, images, or map navigation information generated based on movement trajectory analysis to assist in the retrieval.

[0070] Optionally, based on this trajectory, the server generates a notification message containing information such as "last known location" and "possible destination" and pushes it to the owner's terminal.

[0071] The aforementioned method for recovering lost items in scenic areas involves acquiring the characteristics of the lost item and its spatiotemporal information. Based on the item's characteristics, a matching process is performed in the lost and found database. If no matching is found, at least one surveillance video to be retrieved is identified in the scenic area's monitoring system based on the spatiotemporal information. The tracking target is then determined within each of the videos to be retrieved, and cross-camera trajectory tracking is performed to generate a movement trajectory. Based on the movement trajectory, a lost item retrieval notification is generated and pushed to the owner's device. This combination of image recognition and cross-camera trajectory tracking technology creates an intelligent lost item retrieval process. This process can quickly locate the target from massive amounts of monitoring data and reconstruct its movement path after an item is lost, providing the owner with a clear direction for finding it. This improves the efficiency and likelihood of recovering lost items in complex scenic environments and solves the problem of low retrieval efficiency caused by passively waiting for finders to hand over items or manually reviewing surveillance footage in traditional technologies.

[0072] In an exemplary embodiment, the lost spatiotemporal information includes the lost location and the lost time. Based on the lost spatiotemporal information, at least one surveillance video to be retrieved is determined in the scenic area's monitoring system, including: determining a spatial search range based on the lost location, and determining a temporal search range based on the lost time; filtering out surveillance cameras within the spatial search range in the scenic area's monitoring system, and extracting video segments within the temporal search range from the surveillance videos corresponding to the surveillance cameras as the surveillance videos to be retrieved.

[0073] The spatial search range can be a geographical area centered on the location where the item was lost, pre-set or dynamically calculated based on the characteristics of the item (e.g., whether it is movable, its speed of movement) and the layout of the scenic area. For example, the range within a radius of 50 meters centered on the location where the item was lost.

[0074] The time search range can be a time period that extends forward and backward from the time of loss, in order to cover possible forgetting or loss processes.

[0075] Among them, surveillance cameras refer to video acquisition devices deployed within the scenic area and connected to a unified monitoring system.

[0076] Among them, a video segment refers to an independent segment cut out from a continuous video stream recorded by a surveillance camera within the spatial search range, based on the temporal search range.

[0077] Optionally, the server parses the lost location (e.g., "near the carousel in the east area of ​​the park"), maps it to geographic coordinates, and generates a spatial search range by combining it with preset rules (e.g., "search radius of 500 meters"). At the same time, it parses the lost time (e.g., "around 2 pm") and extends it before and after by a certain buffer time (e.g., 30 minutes before and after) to generate a time search range. Then, it queries the geographic location metadata database of the scenic area's surveillance cameras, filters out all surveillance cameras whose field of view coverage intersects with the spatial search range, and requests its video storage service for each selected surveillance camera to extract video recordings within the time search range, generating individual surveillance video files to be retrieved.

[0078] In practical applications, efficiency can be improved by narrowing the search scope. In terms of time, the search can be extended by 1-2 hours before and after the time of loss. In terms of space, relevant cameras can be identified based on the inferred location of loss, and the cameras can be prioritized, i.e., cameras closer to the location of loss are searched first, and the search scope is gradually expanded if no cameras are found.

[0079] In this embodiment, through precise filtering in both spatiotemporal dimensions, the monitoring data to be analyzed is rapidly reduced from the massive level of the entire scenic area at all times to a limited range that is strongly correlated with the lost event. This reduces the computational load and processing time of subsequent image recognition and trajectory tracking, enabling a rapid response to lost item retrieval requests and improving the efficiency of lost item retrieval.

[0080] In an exemplary embodiment, determining a tracking target in each surveillance video to be retrieved based on item features includes: identifying at least one item in each surveillance video to be retrieved and extracting the item features of each item; determining the feature similarity between each item and the lost item based on the item features of each item and the item features of the lost item; and determining the tracking target from each item based on the feature similarity.

[0081] The identified objects can refer to the areas in a video frame that may belong to "objects" that are automatically located using target detection algorithms in computer vision.

[0082] The extracted item features can refer to the visual feature vector of each detected item region calculated using a feature extraction algorithm.

[0083] Feature similarity is a numerical metric used to measure the degree of closeness between the features of two items. It can be obtained by calculating cosine similarity, reciprocal Euclidean distance, etc.

[0084] Optionally, the server performs target detection on each frame or at fixed intervals of each surveillance video to be retrieved, outlining all items such as backpacks, mobile phones, and hats in the frame. Then, it extracts features from the image region within each detection frame to obtain the feature vector of the item. Subsequently, it calculates the similarity between the feature vector of the item and the feature vector of the lost item provided by the owner. Finally, the item with the highest feature similarity, or all items with similarity exceeding a preset similarity threshold, can be initially identified as the tracking target.

[0085] In practical applications, AI is used to accelerate the video retrieval process. Specifically, time periods when no one passes by can be skipped first, and a key frame can be extracted and analyzed every N frames. By detecting areas of change in the image, segments where only objects appear or move can be analyzed, thereby reducing the retrieval time from several hours to several minutes.

[0086] In this embodiment, automated and standardized analysis of surveillance video content is achieved, which can replace manual screening to quickly and comprehensively scan all visible items in the video and make accurate comparisons through quantified feature similarity. This ensures that possible clues are not missed due to visual fatigue or human negligence, providing a reliable and objective initial target for subsequent tracking and improving the automation level and reliability of the retrieval process.

[0087] In an exemplary embodiment, determining the tracking target from each item based on feature similarity includes: identifying items with feature similarity greater than a preset similarity threshold as suspected lost items, generating a lost item matching list based on each suspected lost item and pushing it to the owner's terminal; and in response to the owner's item selection operation on the lost item matching list, identifying the suspected lost item selected by the item selection operation as the tracking target.

[0088] The preset similarity threshold is a configurable threshold value used to filter out obviously dissimilar objects and retain candidate objects with higher probability. In this application, the preset similarity threshold can be set to 70%.

[0089] Suspected lost items refer to items whose feature similarity reaches a threshold of 70%, but which have not yet been confirmed by the owner. In practical applications, after identifying suspected lost items, multi-angle verification can be performed from different camera perspectives.

[0090] The lost items matching list can be a list of all suspected lost items displayed in a structured manner (e.g., by image, time and location of occurrence, or similarity).

[0091] Among them, the item selection operation refers to the operation corresponding to the owner clicking on a lost item in the lost item matching list on the terminal interface. For example, the owner checks the selection box corresponding to a lost item and clicks the "Confirm" control.

[0092] Optionally, the server sets a similarity threshold of 70%, marking items exceeding this threshold as "suspected lost items." It can also generate a brief entry for each suspected lost item, including a screenshot, the detected video source and timestamp, and a calculated similarity score. This information is then compiled into a list and pushed to the owner's mobile app. The owner can view the list, compare the items in the screenshots with their lost items, and make a selection. After the owner confirms the selection, the suspected lost item is officially designated as the "tracking target" for this recovery mission.

[0093] In this embodiment, a human-machine collaborative confirmation mechanism is introduced. First, high-probability suspected lost items are quickly screened from a large number of lost items, and then final confirmation is made based on the owner's choice. This effectively avoids the problem of subsequent trajectory tracking deviating from the correct direction due to algorithm misjudgment, ensures the accuracy of the tracking starting point, and helps to improve the overall recovery success rate.

[0094] In an exemplary embodiment, a lost item matching list is generated based on each suspected lost item, including: determining the time correlation and location correlation between each suspected lost item and the lost item; determining the matching degree between each suspected lost item and the lost item based on the feature similarity, time correlation, and location correlation between each suspected lost item and the lost item; and sorting each suspected lost item according to the matching degree from high to low to obtain the lost item matching list.

[0095] Among them, the correlation between the time of loss measures the proximity between the time when the suspected item appeared and the time when the lost item was lost; the smaller the time difference, the higher the correlation.

[0096] Among them, the correlation between the location of the lost item and the location where the item was lost is measured. The closer the distance, the higher the correlation.

[0097] Among them, the matching degree is a comprehensive score, which is obtained by weighted fusion of feature similarity, temporal correlation and location correlation, and is used to comprehensively assess the overall probability that the suspected lost item is indeed the lost item.

[0098] Optionally, for each suspected lost item, the server determines the difference between the time it was found and the time the owner reported it as lost, and maps the difference to a time correlation score. At the same time, it determines the physical distance between the location coordinates where it was found and the location reported by the owner, and maps the physical distance to a location correlation score. Then, according to preset weights (e.g., feature similarity weight 60%, time matching 20%, and location reasonableness 20%), the scores of these three metrics are weighted and calculated to obtain the final matching degree. Finally, all suspected lost items are sorted from high to low according to the matching degree to generate the Top 5 matching results.

[0099] In this embodiment, the retrieval judgment is expanded from a single visual feature similarity to the key contextual dimensions of time and space. Through multi-dimensional comprehensive scoring and sorting, the generated lost item matching list is more in line with logical reasoning, and the most likely suspected lost item options are presented to the owner first. This not only improves the user experience, but also increases the accuracy of lost item confirmation.

[0100] For the ease of understanding of those skilled in the art, Figure 2 This paper provides an intelligent matching process for lost items. Specifically, it obtains the features of lost items, performs item detection on videos, extracts the features of each item appearing in the video, and calculates the similarity between the features of the lost items and the features of each item appearing in the video. If the similarity is greater than 70%, the corresponding items are marked as candidate items. The matching score of each candidate item is verified from multiple perspectives (features, time, and location), and then sorted to generate a Top 5 ranking result, which is pushed to the owner for confirmation.

[0101] In an exemplary embodiment, cross-camera trajectory tracking is performed on the target to generate a motion trajectory, including: identifying at least one target surveillance camera where the target appears, and determining the first appearance time and final disappearance time of the target in each target surveillance camera; acquiring video streams from the first appearance time to the final disappearance time of each target surveillance camera, performing single-target tracking on the target in each video stream, generating a local tracking trajectory corresponding to each target surveillance camera, and extracting the appearance features of the target in each local tracking trajectory; and associating each local tracking trajectory across cameras based on the similarity between appearance features and the temporal connection relationship between each local tracking trajectory to generate a motion trajectory.

[0102] Among them, the target surveillance camera refers to the surveillance camera in the scenic area's monitoring system that has been confirmed to have captured the tracking target after preliminary analysis (such as filtering based on lost spatiotemporal information or based on object feature recognition).

[0103] The first appearance time refers to the earliest timestamp when the tracked target enters the field of view of the target surveillance camera and is successfully detected in the video frame.

[0104] The final disappearance time refers to the last timestamp of the tracked target leaving the field of view of the surveillance camera and disappearing from subsequent video frames.

[0105] Among them, video stream refers to the raw video data continuously recorded by the target surveillance camera within a specific time period.

[0106] Single-target tracking refers to computer vision technology that automatically and continuously locks onto and follows a target in consecutive video frames from a single camera.

[0107] Among them, the local tracking trajectory refers to a series of position coordinate points arranged in chronological order generated in the video stream of a single target surveillance camera by performing single-target tracking on the target. It describes the complete movement path of the target within the field of view of the camera.

[0108] Among them, appearance features refer to the feature vectors that are continuously extracted from the image region where the target is located in the video frame during the tracking process, which can characterize its visual attributes (such as color, texture, and shape).

[0109] The temporal connection relationship refers to the reasonable sequence and continuity of the local tracking trajectories recorded by different cameras on the time axis. For example, the disappearance time of the target in the previous camera should be earlier than the appearance time of the target in the next camera.

[0110] Cross-camera association refers to the process of determining whether the local tracking trajectories recorded by different surveillance cameras belong to the same tracking target based on the similarity of appearance features and the logical relationship of time, and then stitching them together.

[0111] Optionally, the server first identifies all surveillance cameras that have detected the target and marks them as target surveillance cameras. For each target surveillance camera, it locates the first frame (i.e., the first appearance time) and the last frame (i.e., the final disappearance time) in the surveillance video of that target surveillance camera. Then, it requests video stream segments from the video storage system for each target surveillance camera between these two precise time points. For each acquired video stream segment, a single-target tracking algorithm is used to predict and update the position of the target frame by frame, starting from the first appearance time, until it finally disappears, thereby generating a local tracking trajectory under that target surveillance camera. At the same time, during the tracking process, the server periodically extracts high-dimensional appearance features from the image region where the target is located. Finally, it analyzes all local tracking trajectories, for example, determining the similarity of the appearance features of the target in any two trajectories and checking whether their timestamp sequences conform to the logic of spatial movement (e.g., whether the time required to move from the area of ​​camera A to the adjacent area of ​​camera B is reasonable). Local movement trajectories belonging to the same target are associated in spatiotemporal order and spliced ​​together to form a complete movement trajectory spanning the field of view of multiple cameras.

[0112] In practical applications, it is necessary to locate the first time an item appears in the surveillance video, mark its location (specific camera and coordinates), and determine its status. The status of the item may be that it was left on the ground (no one picked it up), picked up (someone picked it up), or moved (location changed). Then, tracking the movement of the item between different cameras includes single-target tracking and cross-camera correlation tracking. In single-target tracking, the item is locked and continuously tracked. In cross-camera correlation tracking, the fields of view of adjacent cameras are correlated, the rationality of time connection is judged, the continuity of features is verified, and all the locations the lost item has passed through are recorded to draw a movement trajectory map.

[0113] In this embodiment, by accurately locating the precise time points when the tracking target appears and disappears in each target surveillance camera, efficient and accurate video data extraction is achieved, avoiding the processing of a large amount of irrelevant video content and significantly improving the efficiency of tracking calculation. By maintaining the consistency of the target's identity within a single target surveillance camera through single-target tracking, and then combining appearance feature similarity and strict temporal logic for cross-camera association, the problem of tracking target confusion caused by changes in viewing angle, obstruction, or camera blind spots is effectively solved. This allows for the accurate reconstruction of the complete movement path of lost items or the person who picked them up within the scenic area, providing a reliable and detailed spatiotemporal evidence chain for quickly locating the final destination of lost items or identifying key contacts, thereby improving the success rate and efficiency of lost item retrieval in complex environments.

[0114] In an exemplary embodiment, the method further includes: during the cross-camera trajectory tracking process, when the tracked target is detected to be picked up, extracting facial features of the picker to obtain the picker's facial features; comparing the picker's facial features with the scenic area's facial database, and if the comparison is successful, obtaining the picker's contact information through the scenic area's facial database; and based on the contact information, pushing item delivery guidance information to the picker's terminal.

[0115] Among them, being picked up is a key event determined by behavior recognition algorithms (such as detecting when an item moves from the ground to a person's hand).

[0116] Among them, facial features are feature vectors extracted from the facial region of the picker in the video frame for identity recognition.

[0117] Among them, the scenic area facial database is a database that legally collects and stores tourists' facial features and identity information (such as mobile phone numbers) through means such as real-name ticket purchase and access control systems, with the consent of the tourists.

[0118] The contact information can be an app account or a mobile phone number.

[0119] The item delivery guidance information includes a description of the lost item, the delivery location (such as the lost and found center) contact information, and a thank-you message.

[0120] Optionally, during cross-camera trajectory tracking, behavior recognition is performed in parallel. When the tracking target (i.e., the item) is identified as being picked up and carried by someone from a stationary state (e.g., on the ground), an event is immediately triggered. From the video frame where the event occurs, the face image of the person picking up the item is located and captured, and their facial features are extracted. Then, the facial features are compared with the scenic area's facial database. If a matching record is found, the pre-stored contact information of the person picking up the item is retrieved from the record, and an item delivery guidance SMS or APP push message is automatically generated and sent to the terminal associated with the contact information.

[0121] In practical applications, when identifying the person who picked up the item, specifically, face detection captures the area of ​​the picker's face; body feature extraction extracts the color of the picker's clothing and body shape; behavior analysis identifies the picker's picking actions and dwell time; and the picker's movement path is tracked. During information processing, facial information needs to be anonymized so that it is used only for retrieval.

[0122] In practical applications, it is also possible to infer the current status of the lost item, inferring it as statically left (still in a certain place without being moved), picked up (being carried by someone), handed in (sent to the lost and found), taken out of the park (taken out of the scenic area), or unknown status (tracking interrupted). It is also possible to continuously monitor the change of the item status. For example, refresh the tracking result every 5 - 10 minutes, push in real time when the item status changes, notify in time when the item is transferred multiple times, and give an emergency reminder when the item approaches the scenic area exit.

[0123] In this embodiment, based on tracking the movement of the item, further identify the picking behavior and lock the picker. By linking with the scenic area face database, a direct and active retrieval intervention is achieved, which helps to shorten the item circulation chain and significantly improve the retrieval efficiency and success rate.

[0124] For the convenience of understanding by those skilled in the art, Figure 3 The item tracking process is provided, which specifically includes: determining the lost time - space range, based on the lost time - space range, retrieving relevant surveillance videos, using AI to identify the item in the surveillance videos, and locating the first appearance position of the lost item. Then judge whether the item is picked up. If it is picked up, track the picker across cameras, identify the picker's identity and push a notification. If it is not picked up, mark the static position of the item and notify the staff to retrieve the lost item on - site and push a notification to the owner.

[0125] In an exemplary embodiment, the method further includes: in the case of a comparison failure, based on the end - point position information of the movement trajectory, generating a co - investigation notice containing the end - point position information and sending it to the terminal of the management system of the scenic area.

[0126] Among them, a comparison failure means that the face features of the picker are not registered and matched in the scenic area face database, or the image quality is too poor to extract effective features.

[0127] Among them, the end - point position information of the movement trajectory refers to the position of the last known camera and its field - of - view coverage area when the target signal is finally lost during cross - camera tracking.

[0128] Among them, the co - investigation notice can be a structured work task sheet, which details the lost item information, the last appearance position and time, and is used to request the scenic area staff to conduct on - site verification or pay attention.

[0129] Optionally, when the face comparison fails, the server retrieves the generated movement trajectory, analyzes the last valid coordinate point of the trajectory (i.e., the position of the surveillance point where the tracking target was last seen), and generates an electronic co - investigation notice, the content of which includes the item description, the last appearance time and the precise position (for example, Camera No. 3, 2:35 p.m., near the east - side bench of the central square fountain), and sends it to the dedicated terminal or work platform of the scenic area security or customer service management staff.

[0130] In this embodiment, a reliable backup channel for manual intervention is provided for the automated retrieval process. When the finder cannot be contacted directly, staff can be directly guided to conduct targeted area patrols or inquiries, avoiding blind searching and improving the overall retrieval success rate.

[0131] To facilitate understanding by those skilled in the art, the following method provides an intelligent matching and information push notification method for finding lost items. Specifically, when a tracked target is identified as being picked up, the facial features of the finder are extracted, and facial recognition is performed by matching the park entry record and linking it to the ticket or APP account to query the finder's contact information, with a success rate of approximately 60%-70%. Furthermore, information can be pushed to potential finders. If the finder's identity has been confirmed, a direct APP notification can be sent, or a text message can be sent reminding the finder that "you may have found someone else's lost item," along with guidance on returning the item and reward information. If the finder's identity has not been confirmed, information can be displayed on electronic screens in relevant areas of the scenic area, or a broadcast notification can be made, or on-site staff can be notified to search for the item.

[0132] This application also provides a privacy protection mechanism to protect the privacy of the owner and the finder, specifically including: encrypting the owner's information, i.e., not directly displaying the owner's contact information; implementing anonymous communication, i.e., relaying messages through the platform; desensitizing photos, blurring sensitive information when publicly displayed; protecting facial data, i.e., using facial data only for retrieval and not storing or disseminating it; and automatically deleting the tracking data of the lost item after it has been claimed.

[0133] This application also provides a multi-channel information push strategy, including APP push (via the scenic area APP of the picker), SMS notification (via the picker's registered mobile phone number), on-site broadcast (targeting specific areas), electronic screen display (exits, restaurants and other densely populated areas), and staff search (assisted by on-site staff).

[0134] This application also provides an incentive mechanism to encourage finders to voluntarily return found items, including points rewards (points can be redeemed for gifts), coupons (discounts on food or goods), certificates of honor (a "returning lost property" electronic certificate), public commendation (display on the platform with consent), and material rewards (a reward for the owner of valuable items).

[0135] In one exemplary embodiment, the method further includes: receiving claim verification information input by the owner for the lost item; verifying the claim verification information using preset claim verification rules; and initiating the item claim process if the verification is successful.

[0136] Among them, the claim verification information is additional information provided by the owner to prove that they are the rightful owner of the item, such as a more detailed description of the item's characteristics, a photo of the purchase receipt, or special markings.

[0137] The preset claim verification rules are a series of logical judgment conditions used to verify the authenticity of the claim information, such as comparing whether the description is consistent with the details of the physical item and whether the voucher is valid.

[0138] The item claiming process refers to the standardized process by which, after verification, the system guides the owner and the item custodian (such as a lost and found center) to complete the offline handover or mailing arrangement.

[0139] Optionally, once an item is found and delivered to the lost and found center, the server will notify the owner to claim it. The owner uploads the claim verification information via a terminal, and the server automatically performs the verification according to the preset claim verification rules. If all conditions are met, the server will send a verification success instruction to both the owner and the lost and found center simultaneously, and guide both parties to complete the subsequent steps.

[0140] In practical applications, this application provides a process for an intelligent claim verification mechanism, which can be found in [reference needed]. Figure 4 Specifically, this includes multi-dimensional identity verification methods, similar item claiming processing methods, dispute resolution methods, and remote claiming support.

[0141] In multi-dimensional identity verification, a verification mechanism to prevent fraudulent claims is provided, which includes item detail verification, spatiotemporal verification, and multi-factor verification. Item detail verification is achieved by asking for internal characteristics of the item (such as what cards are in the wallet), describing hidden identifiers (such as tags inside a backpack), and providing purchase vouchers or photos. Spatiotemporal verification is achieved by verifying whether the owner was at the location where the item was lost at the time of loss and by comparing travel trajectories. Multi-factor verification is achieved by using mobile phone verification codes, facial recognition, and checking ticket purchase records.

[0142] In the process of reclaiming similar items, the owner can be asked to provide high-resolution, multi-angle photos of the lost item and point out its unique characteristics. By comparing the photos provided by the owner with the actual item at the scene, the wrong item can be effectively prevented from being claimed.

[0143] In the dispute resolution process, when multiple people claim the same item, the claiming process is first suspended, and all parties are required to provide proof. A comprehensive score is given based on the accuracy of the description, the consistency of time and space, and the sufficiency of the evidence. The decision is made through manual review, and the appeal channel is retained.

[0144] In the remote claim support, for tourists who have already entered the park, multiple methods are available to enable tourists who have left the park to claim their items remotely, such as online identity verification, mail return (cash on delivery or prepayment), entrusting others to claim on their behalf (authorization verification), and collection during their next visit (long-term storage).

[0145] In this embodiment, the security of item ownership can be ensured while efficiently retrieving the item, effectively preventing fraudulent claims.

[0146] In an exemplary embodiment, the method further includes: upon detecting that any person leaving the scenic area, obtaining information on the items carried by the person upon entry and the items carried upon exit; determining whether the person left any items in the scenic area based on the information on the items carried upon entry and the items carried upon exit; and if it is determined that the person left any items in the scenic area, generating an item loss notification message and pushing it to the person's terminal.

[0147] Visitors entering the park refer to tourists who enter the scenic area through facilities such as turnstiles.

[0148] Among them, the information on items carried into / out of the park is collected by smart sensing devices deployed at entrances and exits, which is a list or feature overview of the items carried by tourists when entering and leaving the park.

[0149] Among them, "missing items" refers to items that appear on the admission list but not on the exit list.

[0150] Among them, the item loss alert message is an alarm message used to remind tourists that they may have forgotten items.

[0151] Optionally, when a visitor enters the park, a smart camera next to the gate scans the visitor's visible items (such as backpacks, cameras, strollers) and generates a list of characteristic hash values, which is linked to the visitor's entry record. When the visitor leaves the park, the same device scans again to generate a list of exit items. The server compares these two lists when the visitor leaves the park. If a certain characteristic item from the entry point is found to be missing from the exit list, and no item is detected to have been discarded near the exit, it is determined that the item may have been missed. A notification is then sent to the visitor's device, such as, "The system has detected that your blue backpack may not have been taken out of the park. Please check if you have forgotten it."

[0152] In this embodiment, by conducting automated item checks at the last moment before tourists leave the scenic area, the risk of omissions can be proactively detected and timely reminders can be given. This not only improves the tourist experience but also reduces the pressure on the scenic area's subsequent lost and found management.

[0153] In an exemplary embodiment, the method further includes: obtaining the identity characteristics of the person entering the park, and obtaining the environmental characteristics of the environment in which the person is currently located; generating a personalized reminder strategy for the person in the current environment based on the identity characteristics and environmental characteristics; and generating personalized prompt information based on the personalized reminder strategy and pushing it to the person's terminal.

[0154] The identity characteristics include, but are not limited to, the tourist's age (such as children or the elderly), group type (such as families or individuals), and historical behavioral data (such as records of loss).

[0155] Among them, environmental characteristics include the real-time crowding level of the area where tourists are located, the area attributes (such as water sports area, catering area, amusement facility area), weather conditions, etc.

[0156] The personalized reminder strategy is based on the benchmark of "who is more likely to lose what type of items in what environment", and dynamically generates reminder content, timing and frequency rules.

[0157] Personalized reminder messages are specific reminders generated based on personalized reminder strategies, such as "Dear parents, the playground area is crowded. Please take good care of your child's belongings."

[0158] Optionally, the server combines ticketing systems, location systems, and real-time monitoring and analysis to obtain tourists' identity tags and real-time environmental status, and matches corresponding strategies based on a preset rule engine, thereby sending reminders to tourists at appropriate times.

[0159] For example, based on the current tourist's identity characteristics "bringing a child" and the environmental characteristics "crowded amusement park", a strategy is generated to "send a gentle reminder 10 minutes after entering the area", and a reminder message is pushed to the current tourist's terminal via APP or SMS at the appropriate time.

[0160] In this embodiment, the specific risks of different tourists in different scenarios can be identified, and personalized anti-loss reminders can be provided for each individual.

[0161] This application also provides a full-process management method for lost items, which includes four parts: lost item warehousing management, automatic matching of lost items in the warehouse, overdue processing, and data statistical analysis.

[0162] When managing lost items, we follow the standardized management process for items that have been handed in, which is achieved through photo archiving (multi-angle high-definition photos), feature entry (detailed description of item characteristics), classified storage (stored in partitions according to category and value), number management (unique number), and status tracking (pending claim / claimed / processed).

[0163] When lost items are automatically matched into the inventory, the lost items are compared with the list of items to be claimed. If the similarity is greater than 80%, the item is automatically pushed to the owner, and potential owners are proactively notified to reduce backlog and improve efficiency.

[0164] If the lost items are not claimed within 30 days after the expiration date, they will be kept in long-term custody. If they are not claimed within 90 days, valuable items will be handed over to the police, and ordinary items will be donated to charity or auctioned. The owner will be notified multiple times in advance, and the entire process will be recorded and traceable.

[0165] During data statistical analysis, we identify high-frequency lost item types, generate heat maps of easily lost areas, determine the distribution patterns of lost items during different time periods, and analyze the success rate of retrieval to improve scenic area management.

[0166] This application also provides preventative reminder services, including reminders for easily lost areas, intelligent baggage detection services, reminders for valuables, and personalized reminder services.

[0167] Regarding reminders for easily lost areas, first identify areas prone to loss (such as rest areas, restaurants, and restrooms), then send a reminder when tourists enter: "Items that are easily lost here, please take care of them." When tourists leave, remind them to check their belongings and display a friendly reminder on the electronic screen.

[0168] Regarding the intelligent luggage detection service, the system can record the items carried by tourists when they enter the park (such as the number of backpacks), and compare and check them when they leave the park. If anything is missing, it will immediately alert the tourist and guide them back to find it.

[0169] The valuables alert service is designed to provide special attention to valuables. By identifying valuables (cameras, laptops, etc.), it regularly sends reminders such as "Please check if your valuables are with you." It also provides early warnings for valuables that have not been moved for a long time and offers temporary storage services for valuables.

[0170] Regarding personalized reminder services, reminder messages can be customized based on tourist characteristics. For example, the frequency of reminders can be increased for the elderly and children, special attention reminders can be added for those carrying valuables, reminders can be strengthened for tourists with a history of losing items, and on rainy days, all tourists can be reminded to pay attention to items that are easily forgotten, such as umbrellas.

[0171] In another embodiment, such as Figure 5 As shown, a method for recovering lost items in scenic areas is provided. Taking the application of this method to a server as an example, the method includes the following steps:

[0172] Step S502: Obtain the item characteristics and spatiotemporal information of the lost item.

[0173] Step S504: Based on the characteristics of the item, perform a lost item matching in the lost and found database. If no lost item is matched, then based on the spatiotemporal information of the loss, determine at least one surveillance video to be retrieved in the scenic area's monitoring system.

[0174] Step S506: Identify at least one item in each surveillance video to be retrieved, and extract the item features of each item.

[0175] Step S508: Determine the feature similarity between each item and the lost item based on the item characteristics of each item and the item characteristics of the lost item.

[0176] Step S510: Based on the similarity of each feature, determine the tracking target from each item, and perform cross-camera trajectory tracking on the tracking target to generate a movement trajectory.

[0177] Step S512: Based on the movement trajectory, generate a lost item retrieval notification message and push it to the owner's terminal.

[0178] It should be noted that the specific limitations of the above steps can be found in the above description of the specific limitations of a method for recovering lost items in a scenic area.

[0179] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0180] The following describes the lost and found device for scenic areas provided in the embodiments of this application. The lost and found device for scenic areas has the same inventive concept as the lost and found method for scenic areas described above. The solution provided by the device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the lost and found device for scenic areas provided below can be referred to the limitations of the lost and found method for scenic areas above. The lost and found device for scenic areas described below and the lost and found method for scenic areas described above can be referred to each other, and will not be repeated here.

[0181] In one exemplary embodiment, Figure 6 This is a schematic diagram of the structure of a lost and found device in a scenic area, provided in an embodiment of this application. Figure 6 As shown, the lost and found device in the scenic area includes: an acquisition module 602, a matching module 604, a tracking module 606, and a push module 608, wherein:

[0182] The acquisition module 602 is used to acquire the item characteristics and spatiotemporal information of the lost item.

[0183] The matching module 604 is used to match lost items in the lost and found database based on the characteristics of the items. If no lost item is matched, at least one surveillance video to be retrieved is determined in the scenic area's monitoring system based on the spatiotemporal information of the loss.

[0184] The tracking module 606 is used to determine the tracking target in each surveillance video to be retrieved based on the characteristics of the object, and to perform cross-camera trajectory tracking on the tracking target to generate a movement trajectory;

[0185] The push module 608 is used to generate lost item retrieval prompts based on the movement trajectory and push them to the owner's terminal.

[0186] In one embodiment, the lost spatiotemporal information includes the lost location and the lost time. The matching module 604 is specifically used to determine the spatial search range based on the lost location and the temporal search range based on the lost time. The monitoring system of the scenic area filters out the monitoring cameras within the spatial search range and extracts the video segments within the temporal search range from the monitoring videos corresponding to the monitoring cameras as the monitoring videos to be retrieved.

[0187] In one embodiment, the tracking module 606 is specifically used to identify at least one item in each surveillance video to be retrieved and extract the item features of each item; determine the feature similarity between each item and the lost item based on the item features of each item and the item features of the lost item; and determine the tracking target from each item based on the feature similarity.

[0188] In one embodiment, the tracking module 606 is specifically used to identify items with feature similarity greater than a preset similarity threshold as suspected lost items, and generate a lost item matching list based on each suspected lost item and push it to the owner's terminal; in response to the owner's item selection operation on the lost item matching list, the suspected lost item selected by the item selection operation is identified as the tracking target.

[0189] In one embodiment, the tracking module 606 is specifically used to determine the correlation between the time of loss and the location of loss between each suspected lost item and the lost item; determine the matching degree between each suspected lost item and the lost item based on the feature similarity, time of loss and location of loss between each suspected lost item and the lost item; and sort each suspected lost item according to the matching degree from high to low to obtain a list of matching lost items.

[0190] In one embodiment, the tracking module 606 is configured to determine at least one target surveillance camera where the tracked target appears, and determine the first appearance time and final disappearance time of the tracked target in each target surveillance camera; acquire the video stream of each target surveillance camera from the first appearance time to the final disappearance time, perform single-target tracking of the tracked target in each video stream, generate a local tracking trajectory corresponding to each target surveillance camera, and extract the appearance features of the tracked target in each local tracking trajectory; based on the similarity between each appearance feature and the temporal connection relationship between each local tracking trajectory, perform cross-camera association of each local tracking trajectory to generate a motion trajectory.

[0191] In one embodiment, the device further includes: a face recognition module, used to extract the face features of the picker when the tracked target is detected to be picked up during cross-camera trajectory tracking, and obtain the face features of the picker; compare the face features of the picker with the scenic area's face database, and if the comparison is successful, obtain the picker's contact information through the scenic area's face database; and based on the contact information, push item delivery guidance information to the picker's terminal.

[0192] In one embodiment, the device further includes: a cooperation module, used to generate a cooperation notification containing the destination location information based on the destination location information of the movement trajectory and send it to the terminal of the scenic area's management system in the event of a comparison failure.

[0193] In one embodiment, the device further includes: a lost owner identity verification module, used to receive the claim verification information input by the lost owner for the lost item; to verify the claim verification information using preset claim verification rules; and to initiate the item claim process if the verification is successful.

[0194] In one embodiment, the device further includes: a lost item detection module, used to obtain information on items carried into the park and items carried out of the park when any visitor is detected leaving the park; to determine whether the visitor has left any items in the park based on the information on items carried into the park and items carried out of the park; and to generate a lost item notification message and push it to the visitor's terminal when it is determined that the visitor has left any items in the park.

[0195] In one embodiment, the device further includes: a personalized prompt module, used to acquire the identity characteristics of the person entering the park, and to acquire the environmental characteristics of the environment in which the person is currently located; to generate a personalized reminder strategy for the person entering the park in the current environment based on the identity characteristics and environmental characteristics; and to generate personalized prompt information based on the personalized reminder strategy and push it to the person's terminal.

[0196] In one exemplary embodiment, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the scenic area lost item retrieval methods described above.

[0197] In one exemplary embodiment, this application also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of any of the lost and found methods in the above embodiments.

[0198] In one exemplary embodiment, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the scenic area lost item retrieval methods described above.

[0199] Indicatively, such as Figure 7 As shown, Figure 7 This is a schematic diagram of the internal structure of a computer device 700 provided in an embodiment of this application. The computer device 700 can be provided as a server. (Refer to...) Figure 7 The computer device 700 includes a processing component 702, which further includes one or more processors, and memory resources represented by memory 701 for storing instructions, such as application programs, that can be executed by the processing component 702. The application programs stored in memory 701 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 702 is configured to execute instructions to perform the scenic area lost item retrieval method of any of the above embodiments.

[0200] The computer device 700 may also include a power supply component 703 configured to perform power management of the computer device 700, a wired or wireless network interface 704 configured to connect the computer device 700 to a network, and an input / output (I / O) interface 705. The computer device 700 can operate on an operating system stored in memory 701, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0201] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0202] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0203] Finally, it should 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.

[0204] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0205] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for recovering lost items in a scenic area, characterized in that, The method includes: Obtain the item characteristics and spatiotemporal information of the lost item; Based on the characteristics of the item, a lost item matching is performed in the lost and found database. If no lost item is matched, at least one surveillance video to be retrieved is determined in the scenic area's monitoring system based on the lost time and space information. Based on the characteristics of the items, the tracking target is determined in each of the surveillance videos to be retrieved, and cross-camera trajectory tracking is performed on the tracking target to generate a movement trajectory; Based on the movement trajectory, a lost item retrieval notification is generated and pushed to the owner's terminal.

2. The method according to claim 1, characterized in that, The lost spatiotemporal information includes the location and time of loss. Based on this lost spatiotemporal information, determining at least one surveillance video to be retrieved within the scenic area's monitoring system includes: Based on the location of loss, determine the spatial search range; and based on the time of loss, determine the temporal search range. The monitoring system of the scenic area selects the monitoring cameras within the spatial search range, and extracts video segments within the time search range from the monitoring videos corresponding to the monitoring cameras, which are then used as the monitoring videos to be searched.

3. The method according to claim 1, characterized in that, The step of determining the tracking target in each of the surveillance videos to be retrieved based on the characteristics of the items includes: Identify at least one item in each of the surveillance videos to be retrieved, and extract the item features of each item; Based on the characteristics of each item and the characteristics of the lost item, determine the feature similarity between each item and the lost item; The tracking target is determined from each of the items based on the similarity of the described features.

4. The method according to claim 3, characterized in that, The step of determining the tracking target from each of the items based on the similarity of each of the aforementioned features includes: Items with a feature similarity greater than a preset similarity threshold are identified as suspected lost items, and a lost item matching list is generated based on each suspected lost item and pushed to the owner's terminal; In response to the owner's selection of an item from the lost items matching list, the suspected lost item selected in the selection operation is identified as the tracking target.

5. The method according to claim 4, characterized in that, The step of generating a missing item matching list based on each of the suspected missing items includes: Determine the correlation between the time of loss and the location of loss between each of the suspected lost items and the actual lost items; Based on the feature similarity, time-of-loss correlation, and location-of-loss correlation between each suspected lost item and the lost item, the matching degree between each suspected lost item and the lost item is determined. The suspected lost items are sorted according to their matching degree from high to low to obtain the lost item matching list.

6. The method according to claim 1, characterized in that, The step of performing cross-camera trajectory tracking on the tracked target and generating a movement trajectory includes: Identify at least one target surveillance camera where the tracked target appears, and determine the first appearance time and final disappearance time of the tracked target at each of the target surveillance cameras; Acquire video streams from the first appearance time to the final disappearance time of each target surveillance camera, perform single-target tracking on the tracked target in each video stream, generate local tracking trajectories corresponding to each target surveillance camera, and extract the appearance features of the tracked target in each local tracking trajectory; Based on the similarity between the appearance features and the temporal connection between the local tracking trajectories, the local tracking trajectories are correlated across cameras to generate the motion trajectory.

7. The method according to claim 1, characterized in that, The method further includes: During the cross-camera trajectory tracking process, when the tracked target is detected to be picked up, facial features of the picker are extracted to obtain the facial features of the picker. The facial features of the finder are compared with the facial database of the scenic area. If the comparison is successful, the contact information of the finder is obtained through the facial database of the scenic area. Based on the contact information, guide information for delivering the item is pushed to the finder's terminal.

8. A lost and found device for scenic areas, characterized in that, The device includes: The acquisition module is used to acquire the item characteristics and spatiotemporal information of the lost item. The matching module is used to match lost items in the lost and found database based on the characteristics of the items. If no lost item is matched, at least one surveillance video to be retrieved is determined in the scenic area's monitoring system based on the spatiotemporal information of the loss. The tracking module is used to determine the tracking target in each of the surveillance videos to be retrieved based on the characteristics of the item, and to perform cross-camera trajectory tracking on the tracking target to generate a movement trajectory; The push module is used to generate lost item retrieval prompts based on the movement trajectory and push them to the owner's terminal.

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, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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