Pedestrian role recognition and cross-camera duplicate removal engineering method

Abstract

The invention discloses a pedestrian role recognition and cross-camera de-duplication engineering method, which relates to the technical field of pedestrian analysis and comprises the following steps of: inputting and preprocessing multiple paths of videos; pedestrian detection and single-camera tracking are carried out; pedestrian feature extraction and role reasoning; cross-camera de-weighting is carried out; global identity and role information management; according to the method, through breakthrough in the two aspects of algorithm innovation and system engineering, many limitations in the prior art are overcome, the core technical index of pedestrian analysis is improved, and more importantly, the practicability, reliability and expandability of the method in a real and complex environment are enhanced; according to the video monitoring system, the whole process processing from video input to result output is realized, the complex challenge of real world monitoring application is effectively handled, and the video monitoring system can better serve diversified application scenes of security, management and business intelligence, so that the video monitoring system is endowed with higher-level intelligence and wider practical value in practical application.

Description

An engineered method for pedestrian role recognition and cross-camera deduplication Technical Field

[0001] This invention relates to the field of pedestrian analysis technology, specifically to an engineered method for pedestrian role recognition and cross-camera deduplication. Background Technology

[0002] As a core component of security, operation management, and public safety, video surveillance technology has undergone profound changes from analog to digital and from passive recording to intelligent analysis. In recent years, with the continuous reduction in the cost of image sensors and computing hardware, as well as the rapid development of artificial intelligence technology, the deployment scale and application depth of video surveillance systems have shown unprecedented growth. The widespread use of large-scale camera networks in various places such as city streets, commercial complexes, transportation hubs, and industrial parks has become the norm. Currently, pedestrian role recognition aims to determine the functional or intentional role played by pedestrians in a specific scenario based on their appearance characteristics, behavior patterns, interaction information, and the context of their environment. For example, in a shopping mall environment, the system needs to be able to distinguish between ordinary customers, sales assistants, cashiers, delivery personnel, and even potentially risky individuals with suspicious behavior. Cross-camera pedestrian deduplication focuses on accurately and uniquely identifying the same pedestrian when they move from the field of view of one camera to the field of view of another within a wide area covered by multiple cameras, avoiding misidentification as multiple different individuals or confusion with other individuals.

[0003] However, traditional video surveillance systems face severe challenges in practical applications. Early systems heavily relied on manual real-time monitoring or post-event video retrieval. Faced with massive amounts of video data generated by cameras, manual processing is not only inefficient but also prone to missing crucial information due to operator fatigue and distraction. This passive monitoring model cannot meet the urgent needs of modern society for rapid response to security incidents, improved operational efficiency, and early warning of potential risks. Furthermore, among the many monitored objects, pedestrians are the most common and complex analysis targets in urban environments and various locations. As application needs deepen, simply detecting and counting pedestrians is far from sufficient. In addition, most existing technologies are limited to recognizing pedestrian appearance attributes, making it difficult to understand the specific role of pedestrians in a scene and their dynamic changes. Current appearance-based pedestrian re-identification... In real-world surveillance environments, this method is susceptible to factors such as occlusion, changes in lighting, differences in viewing angle, and variations in pedestrian clothing, which can lead to incorrect identity association. Frequent occurrences make it difficult to maintain the consistency of pedestrian identity in a large-scale, distributed camera network. Furthermore, existing technologies are often simply a stacking of multiple independent AI modules, lacking overall optimization and deep integration at the system level, resulting in performance bottlenecks, high computing costs, and poor scalability in actual deployments. Summary of the Invention

[0004] This invention provides an engineered method for pedestrian role recognition and cross-camera deduplication, which can effectively solve the problems of pedestrian role recognition and cross-camera deduplication mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an engineered method for pedestrian role recognition and cross-camera deduplication, based on multi-cue fusion and deep learning, for pedestrian role recognition and cross-camera deduplication in surveillance videos, realizing full-process processing from video input to result output, effectively addressing the complex challenges of real-world surveillance applications;

[0006] Includes the following steps:

[0007] Step 1: Multi-channel video input and preprocessing;

[0008] Step two: pedestrian detection and single-camera tracking;

[0009] Step 3: Pedestrian feature extraction and role reasoning;

[0010] Step 4: Deduplicate across cameras;

[0011] Step 5: Global Identity and Role Information Management;

[0012] Step 6: Output the results and interface with the application.

[0013] According to the above technical solution, in step one, the system receives video streams in real time from multiple surveillance cameras deployed in different locations, and performs necessary preprocessing on the input video streams to improve the quality of subsequent analysis. Specific preprocessing methods include decoding, frame synchronization, and image enhancement.

[0014] According to the above technical solution, in step two, each preprocessed video stream is sent to a high-performance pedestrian detector module. This module uses an advanced deep learning target detection algorithm to detect pedestrian targets in real time in each frame of the video and output their bounding box coordinates and detection confidence.

[0015] Next, within the field of view of each independent camera, the detected pedestrians are continuously tracked using a single-camera multi-target tracking algorithm. A continuous motion trajectory segment is generated for each pedestrian within the current camera's field of view, and a temporary camera ID is assigned to it.

[0016] According to the above technical solution, in step three, for each pedestrian or its trajectory segment successfully tracked within a single camera, the system will call the multi-clue feature extractor module to extract a rich set of feature descriptors from its image sequence and motion information;

[0017] The appearance features are extracted using a deep convolutional neural network to extract global and salient local visual appearance features of pedestrians. These features are robust to changes in lighting and viewing angle.

[0018] Fine-grained semantic attributes are identified through a dedicated pedestrian attribute recognition submodule, which recognizes multiple semantic attributes of pedestrians;

[0019] Predefined roles refer to the predefined trajectory features of some roles, mainly clothing and appearance. A feature comparison library is established, and the trajectory of a specific role is determined by comparing feature similarity.

[0020] Interaction feature analysis analyzes pedestrian interaction behavior in a scene, including: interaction with predefined areas, interaction with other identified objects in the scene, and social interaction with other pedestrians. These features can be used to uniformly update roles after the trajectory is re-identified to obtain a unique pedestrian ID.

[0021] According to the above technical solution, in step three, after the above features are integrated, they are input into the core pedestrian role reasoning engine PRIE. PRIE adopts a multi-level decision engine based on business logic. According to the preset role definition library, combined with the multi-dimensional information input in real time, it assigns one or more most likely role labels to each observed pedestrian and gives the corresponding confidence level.

[0022] For certain roles that require long-term observation to determine, PRIE will combine historical tracking data to make a judgment;

[0023] All the above feature descriptors are divided into two categories: quantitative features and attribute / behavioral features. Quantitative features are... To describe, attribute behavioral characteristics are used To express;

[0024] In addition to the two types of features mentioned above, it also retains the high-dimensional features that characterize the human body, using These features serve as the necessary basis for calculations, are utilized in subsequent steps, and are ultimately integrated into unified role information and pedestrian tags.

[0025] According to the above technical solution, step four specifically involves pedestrian re-identification. When a pedestrian appears within the field of view of a certain camera or their trajectory segment ends, the extracted multimodal features are sent to the cross-camera deduplication module. ;

[0026] First, these features are efficiently compared with a feature database of all globally unique pedestrian identities already existing in the system. The matching algorithm strategy employed aims to overcome the limitations of traditional methods. The shortcomings are that it is divided into several steps with precise semantics. For ease of description, the following variables and functions are set:

[0027] Pedestrian features are composed of the features from step three:

[0028] ;

[0029] Determination of the characteristics of pedestrian groups:

[0030] ;

[0031] in, Representing specific feature conditions, the returned value is... These represent conditions not being met and conditions being met, respectively.

[0032] Similarity determination:

[0033] ;

[0034] in, Represents the similarity threshold. It's a vector similarity function; a commonly used one is cosine similarity, which returns... These represent insufficient similarity and sufficient similarity, respectively.

[0035] Aggregate decision function:

[0036] ;

[0037] in, Represents the similarity threshold, and the returned values ​​are... These represent unaggregated and aggregated elements, respectively. Specifically, the function's decision logic is as follows:

[0038] .

[0039] According to the above technical solution, step four uses the above definitions to more clearly describe and compare each of the following clustering steps, including high similarity trajectory aggregation, high similarity aggregation in the same direction trajectory, similarity aggregation in the same direction of isolated trajectories, similarity aggregation in opposite directions trajectory, low similarity aggregation of isolated trajectories with the same attribute, fallback matching and recall of special attribute trajectories, and time and space constraints.

[0040] High similarity trajectory aggregation: First, using high similarity, the trajectories are initially aggregated with high confidence, and the most similar trajectories of the same person are initially aggregated together;

[0041] High similarity aggregation of same-direction trajectories: Since same-direction trajectories of the same person often have high similarity, medium to high similarity also has high confidence under the premise of same-direction trajectory aggregation;

[0042] Similarity aggregation in the same direction for isolated trajectories: Generally, isolated, unmatched trajectories are due to certain anomalies. In this case, the similarity threshold can be lowered and similarity aggregation can be performed, which can significantly reduce the rate of missed aggregation of trajectories in the same direction.

[0043] Similarity aggregation in dissimilar trajectories: Dissimilar trajectories of the same person often do not have high similarity. Therefore, when aggregating dissimilar trajectories, a medium similarity threshold is often required. After this step, the aggregation of most trajectories has been completed.

[0044] Clustering of isolated trajectories with low similarity: After the previous step, most trajectories have been successfully clustered, leaving a small number of isolated un-clustered trajectories. These trajectories can only be clustered with low similarity. To avoid mis-clustering, the trajectory attribute features from the feature extraction and role reasoning stages need to be included.

[0045] Special attribute trajectory fallback matching recall: Some trajectories have special attributes. Depending on the specific needs, a fallback recall step is added, using a relatively low threshold and more lenient conditions to ensure a high recall rate for these trajectories.

[0046] Temporal and spatial constraints: These can be added as needed at each step of the aggregation process. When the similarity threshold is at or below the middle threshold, adding temporal and spatial constraints can greatly reduce the number of trajectory pairs that need to be matched and increase the aggregation accuracy.

[0047] According to the above technical solution, in step five, the system maintains a central or distributed database to store and manage the globally unique identity IDs of all pedestrians, trajectory segments from different cameras associated with each ID, role labels assigned at each time point or time period, and various feature vectors used for identification and matching. This database supports efficient query and update operations, enabling upper-layer applications to easily retrieve the complete activity history of any individual in the monitoring network.

[0048] Furthermore, to ensure data consistency for business purposes, each aggregated pedestrian trajectory group needs to undergo unified label reassignment. Different reassignment strategies are employed for two different types of feature labels, specifically: quantitative features... and attribute behavioral characteristics ;

[0049] Quantitative characteristics Assume the first The integration threshold for each feature is Then after redistribution The tags are:

[0050] ;

[0051] in, For smoothing statistical functions, common choices include the mean and median, ultimately returning... These represent whether the tag is active or not;

[0052] Attributes and behavioral characteristics After redistribution The tags are:

[0053] ;

[0054] The mode is generally used as the aggregation function for categorical variables, but the mode may return multiple values. Therefore, an outer function with a unique value is needed. A priority order can be set when generating tie candidate tags according to business needs.

[0055] According to the above technical solution, in step six, the system provides various forms of output to the upper-layer application and user interface through standardized interfaces based on the analysis results, including real-time alarms, visualization, data reports and dashboards, open APIs, and overall system data flow.

[0056] Real-time alerts are triggered when a specific role or a combination of specific roles exhibits specific behaviors.

[0057] Visualization displays the pedestrian's movement trajectory, bounding box, global ID, and currently identified role label overlaid on an electronic map or real-time camera feed.

[0058] The data reports and dashboards mainly generate various statistical reports and visual dashboards related to pedestrian traffic statistics, heat maps of specific role activities, customer flow analysis, and employee work efficiency evaluation.

[0059] The open API primarily provides API interfaces that allow third-party applications or platforms to integrate and invoke pedestrian analysis capabilities;

[0060] The entire process of the system's data flow reflects the gradual refinement from raw data to semantic information.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] 1. By integrating multi-dimensional cues such as appearance, fine-grained attributes, trajectory patterns, interaction information, and context, it can more accurately and meticulously identify the real roles of pedestrians in complex scenes. It can also identify roles defined based on complex time patterns and interaction behaviors. Compared with existing technologies that mainly rely on simple attribute labels or basic behavior judgments, this is something that most current PAR systems cannot achieve. It greatly enhances the ability to deeply understand scene dynamics and significantly improves the accuracy and precision of pedestrian role recognition.

[0063] 2. By employing a novel multimodal feature fusion matching strategy, an advanced graph association algorithm, and an auxiliary verification mechanism for role consistency, the system achieves... It can significantly reduce identity hopping during cross-camera tracking. Error rate, and when faced with real-world challenges such as occlusion, drastic changes in lighting, and significant differences in perspective. The matching accuracy has been significantly improved, outperforming traditional methods that rely solely on appearance features. This enhances the ability to maintain the consistency of pedestrian identity over long-term tracking, providing a more reliable foundation for subsequent behavioral analysis and trajectory reconstruction applications.

[0064] 3. Through modular design, optimized data pipeline, and edge-cloud collaborative computing architecture, it can efficiently process video data from large-scale camera networks, supporting real-time or near real-time analysis and response. This makes it suitable for city-level or large-scale facility monitoring deployment needs. Compared to simply stacking multiple unoptimized AI models, the integrated design reduces the processing overhead per camera / video stream, improves resource utilization, and the highly automated analysis capabilities will greatly reduce the reliance on manual real-time monitoring and post-event video retrieval, freeing security and management personnel from tedious monitoring tasks and allowing them to focus on higher-level decision-making, response, and planning.

[0065] 4. By accurately identifying and tracking individuals with different roles and analyzing their behavioral patterns, businesses can gain refined data insights to help optimize product display, improve customer experience, evaluate service quality, and identify operational bottlenecks, thereby improving business efficiency. Furthermore, in-depth analysis of the overall role composition, flow patterns, gathering areas, and interactive behaviors of the population helps to better understand the dynamics of people in public places, providing a scientific basis for security, crowd control, and emergency plan development for large-scale events.

[0066] In summary, breakthroughs in both algorithm innovation and systems engineering have overcome many limitations of existing technologies. This has not only improved the core technical indicators of pedestrian analysis but, more importantly, enhanced its practicality, reliability, and scalability in real and complex environments. As a result, it can better serve diverse application scenarios in security, management, and business intelligence, thereby endowing video surveillance systems with a higher level of intelligence and broader practical value in practical applications. Attached Figure Description

[0067] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0068] In the attached diagram:

[0069] Figure 1 is a flowchart of the steps of the method of the present invention;

[0070] Figure 2 is a schematic diagram of the pedestrian role reasoning engine PRIE of the present invention;

[0071] Figure 3 is a schematic diagram of the cross-camera deduplication engine CCDM of the present invention;

[0072] Figure 4 is a schematic diagram of the overall system data flow of the present invention. Detailed Implementation

[0073] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0074] Example: As shown in Figure 1, the present invention provides a technical solution, an engineered method for pedestrian role recognition and cross-camera deduplication, which is based on multi-cue fusion and deep learning to recognize pedestrian roles and deduplicate across cameras in surveillance videos, realizing full-process processing from video input to result output, effectively addressing the complex challenges of real-world surveillance applications;

[0075] Includes the following steps:

[0076] Step 1: Multi-channel video input and preprocessing;

[0077] Step two: pedestrian detection and single-camera tracking;

[0078] Step 3: Pedestrian feature extraction and role reasoning;

[0079] Step 4: Deduplicate across cameras;

[0080] Step 5: Global Identity and Role Information Management;

[0081] Step 6: Output the results and interface with the application.

[0082] Based on the above technical solution, in step one, the system receives video streams in real time from multiple surveillance cameras deployed in different locations, specifically including... Flow, from The system acquires and performs necessary preprocessing on the input video stream to improve the quality of subsequent analysis. Specific preprocessing methods include decoding, frame synchronization, and image enhancement. Decoding involves decoding the input video stream. Frame synchronization refers to the existence of a time synchronization mechanism between cameras. Image enhancement includes noise reduction and low-light enhancement, depending on the situation.

[0083] Based on the above technical solution, in step two, each preprocessed video stream is sent to a high-performance pedestrian detector module. This module employs an advanced deep learning object detection algorithm, specifically optimized for surveillance scenarios. A series of variants that detect pedestrian targets in real time in every frame of the video and output their bounding box coordinates and detection confidence;

[0084] Next, multi-target tracking is performed using a single camera within the field of view of each individual camera. The algorithm continuously tracks detected pedestrians, specifically employing improved methods. Generate a continuous motion trajectory segment for each pedestrian within the current camera's field of view. It is then assigned a temporary camera ID.

[0085] Based on the above technical solution, in step three, for each pedestrian successfully tracked within a single camera, the system will call the multi-clue feature extractor module to extract a rich set of feature descriptors from their image sequence and motion information, including appearance features, fine-grained semantic attributes, predefined roles, and interaction features.

[0086] Appearance features This study utilizes deep convolutional neural networks to extract global and salient local visual appearance features of pedestrians, employing existing... The backbone network is improved, specifically through novel improvements. The architecture, with these features, is robust to changes in lighting and viewpoint;

[0087] Fine-grained semantic attributes Through specialized pedestrian attribute recognition The submodule identifies various semantic attributes of pedestrians. The submodule is a network based on multi-task learning. The semantic attributes include gender, age group, color and style of clothing, whether wearing glasses, whether carrying a backpack, and hairstyle.

[0088] Predefined roles This refers to predefining the trajectory features of some characters, mainly their clothing and appearance, establishing a feature comparison library, and determining the trajectory of a specific character through feature similarity comparison.

[0089] Interactive features The system analyzes pedestrian interaction behaviors in a scene, including interactions with predefined areas, interactions with other identified objects in the scene, and social interactions with other pedestrians. Interactions with predefined areas specifically include entering and leaving a specific area and the time spent in the area. Interactions with other identified objects in the scene specifically include other identified objects, such as shelves and access control systems. Social interactions with other pedestrians include distance, relative orientation, and synchronized movement patterns. These features can be used to uniformly update the role after the trajectory is re-identified to obtain a unique pedestrian ID.

[0090] As shown in Figure 2, based on the above technical solution, in step three, after the above features are integrated, they are input into the core pedestrian role inference engine. Internally, a multi-layered decision engine based on business logic is used. According to the preset role definition library, combined with real-time input of multi-dimensional information, each observed pedestrian is assigned a most likely role label and given a corresponding confidence level. The preset role definition library includes: customers, employees and loitering. Customers are usually seen browsing in the merchandise area and making purchases. Employees wear specific uniforms, work in fixed areas or provide services to others. Loitering stay in sensitive areas for a long time and their behavior has no clear purpose.

[0091] For certain roles that require long-term observation to determine their suitability... The judgment will be made based on historical tracking data. This type of role includes regular customers and individuals with persistent suspicious behavior.

[0092] All the above feature descriptors are divided into two categories: quantitative features and attribute / behavioral features. Quantitative features specifically include distance similarity scores compared to the employee feature database. To describe it, the specific attributes and behavioral characteristics include the color of the upper garment, whether a hat is worn, and the direction of the trajectory. To express;

[0093] In addition to the two types of features mentioned above, it also retains the high-dimensional features that characterize the human body, using These features serve as the necessary basis for calculations, are utilized in subsequent steps, and are ultimately integrated into unified role information and pedestrian tags.

[0094] As shown in Figure 3, based on the above technical solution, step four specifically involves pedestrian re-identification. When a pedestrian appears within the field of view of a certain camera, the extracted multimodal features are sent to the cross-camera deduplication module. Multimodal features, especially robust appearance features, partially stable attribute features, as well as possible motion patterns and preliminary role tendencies;

[0095] First, these characteristics are compared with all globally unique pedestrian identities already existing in the system. The feature library is used for efficient comparison, and the matching algorithm strategy adopted aims to overcome the limitations of traditional methods. The shortcomings are that it is divided into several steps with precise semantics. For ease of description, the following variables and functions are set:

[0096] Pedestrian features are composed of the features from step three:

[0097] ;

[0098] Determination of the characteristics of pedestrian groups:

[0099] ;

[0100] in, This represents specific characteristic conditions, such as whether the trajectories are in the same direction, whether they are isolated, and whether the spatiotemporal constraints are met. The returned value is... These represent conditions not being met and conditions being met, respectively.

[0101] Similarity determination:

[0102] ;

[0103] in, Represents the similarity threshold. It's a vector similarity function; a commonly used one is cosine similarity, which returns... These represent insufficient similarity and sufficient similarity, respectively.

[0104] Aggregate decision function:

[0105] ;

[0106] in, Represents the similarity threshold, and the returned values ​​are... These represent unaggregated and aggregated elements, respectively. Specifically, the function's decision logic is as follows:

[0107] .

[0108] Based on the above technical solution, in step four, the above definitions are used to more clearly describe and compare each of the following clustering steps, including high similarity trajectory aggregation, high similarity aggregation among trajectories in the same direction, similarity aggregation among isolated trajectories in the same direction, similarity aggregation among trajectories in opposite directions, low similarity aggregation of isolated trajectories with the same attribute, fallback matching and recall of special attribute trajectories, and time and space constraints.

[0109] High-similarity trajectory aggregation: First, using high similarity, preliminary high-confidence aggregation is performed on the trajectories, initially aggregating the most similar trajectories of the same person. Specifically, here we set... That is, without using any feature-based decision conditions, and Then set it to a higher value, specifically, ;

[0110] High similarity aggregation of trajectories in the same direction: Since trajectories of the same person in the same direction often have high similarity, under the premise of trajectories aggregation in the same direction, medium to high similarity also has a high confidence level. Specifically, we set... The trajectory direction; if the trajectory directions are the same, then... return ,and Then set it to a medium-high threshold, specifically, ;

[0111] Isolated trajectory similarity aggregation in the same direction: Generally, isolated, unmatched trajectories are due to certain anomalies, such as occlusion, lighting conditions, or angles that do not meet similarity requirements. In such cases, lowering the similarity threshold and implementing medium-similarity aggregation can significantly reduce the missed aggregation rate of trajectories in the same direction. Specifically, setting... For trajectory direction and isolation, if the trajectories have the same direction and are all isolated trajectories, then... return Isolated trajectories are those that have not yet been aggregated into trajectory groups, while Then it is set to a medium threshold, specifically, ;

[0112] Similarity aggregation in dissimilar trajectories: Trajectories of the same person in different directions often do not have high similarity. These dissimilar trajectories include frontal and rear views. Therefore, a medium similarity threshold is often used when aggregating dissimilar trajectories. After this step, the aggregation of most trajectories is complete. Specifically, a similarity threshold is set... For trajectory anisotropy, trajectories with different directions will... return ,and Then it is set to a medium threshold, specifically, ;

[0113] Clustering of isolated trajectories with low similarity: After the previous step, most trajectories have been successfully clustered, leaving a small number of isolated, un-clustered trajectories. These can only be clustered using low similarity. To avoid incorrect clustering, the trajectory attribute features from the feature extraction and role inference stages need to be included, specifically age, gender, role, clothing, and backpack. Specifically, the following settings are defined... For trajectory isolation and other various pedestrian attributes, if all pedestrian attributes of a trajectory are the same and all trajectories are isolated, then... return ,and Then set it to a low threshold, specifically, ;

[0114] Special attribute trajectory fallback matching recall: Some trajectories possess special attributes, specifically unique behaviors requiring further data statistics and analysis. If these trajectories are not successfully recalled during aggregation, subsequent data loss can occur. To address this, a fallback recall step is added, using relatively low thresholds and more lenient conditions to ensure a high recall rate for these trajectories. Specifically, this involves setting... For special attributes, if the trajectory carries special attributes, then return ,and Then it is set to an extremely low threshold, specifically, ;

[0115] Temporal and spatial constraints: These can be added as needed at each aggregation step. Under medium threshold conditions, adding temporal and spatial constraints can significantly reduce the number of trajectory pairs requiring matching and increase aggregation accuracy. Temporal and spatial constraints include adjacent cameras and 10-minute intervals before and after. Specifically, any temporal and spatial conditions can be added to each step. As an additional condition, it only applies when the additional spatiotemporal conditions are satisfied. Only then is it possible to return. ;

[0116] Through multi-step trajectory aggregation operations, a trajectory aggregation accuracy rate of over 90% can be guaranteed in most scenarios, providing high-quality data support for subsequent behavior analysis and data statistics.

[0117] Based on the above technical solution, in step five, the system maintains a central database to store and manage the globally unique identity IDs of all pedestrians, trajectory segments from different cameras associated with each ID, role labels assigned at each time point, and various feature vectors used for identification and matching. This database supports efficient query and update operations, enabling upper-layer applications to easily retrieve the complete activity history of any individual in the monitoring network, including their role and trajectory at different times and locations.

[0118] Furthermore, to ensure data consistency for business purposes, each aggregated pedestrian trajectory group needs to undergo unified label reassignment. Different reassignment strategies are employed for two different types of feature labels, specifically: quantitative features... and attribute behavioral characteristics ;

[0119] Quantitative characteristics Assume the first The integration threshold for each feature is Then after redistribution The tags are:

[0120] ;

[0121] in, For smoothing statistical functions, common choices include the mean and median, ultimately returning... These represent whether the tag is active or not;

[0122] Attributes and behavioral characteristics After redistribution The tags are:

[0123] ;

[0124] The mode is generally used as the aggregation function for categorical variables, but the mode may return multiple values. Therefore, an outer function with a unique value is needed. A priority order can be set when generating tie candidate tags according to business needs.

[0125] As shown in Figure 4, based on the above technical solution, in step six, the system provides various forms of output to the upper-layer application and user interface through standardized interfaces according to the analysis results, including real-time alarms, visualization, data reports and dashboards, open APIs, and overall system data flow.

[0126] Real-time alerts are triggered when a specific role or a combination of specific roles and specific behaviors is detected. The specific role is an intruder, and the specific behavior is a customer opening the product packaging in a non-payment area.

[0127] Visualization displays the pedestrian's movement trajectory, bounding box, global ID, and currently identified role label overlaid on the real-time camera feed.

[0128] The data reports and dashboards mainly generate various statistical reports and visual dashboards on pedestrian traffic statistics, heat maps of specific role activities, customer flow analysis, and employee work efficiency evaluation, providing data support for security management, operational decision-making, and business intelligence;

[0129] The open API primarily provides API interfaces that allow third-party applications to integrate and invoke pedestrian analysis capabilities;

[0130] The entire data flow of the system reflects the layer-by-layer extraction and intelligent analysis from raw video data to advanced semantic information, and the close collaboration between modules, achieving efficient collaboration through data flow optimization.

[0131] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An engineered method for pedestrian role recognition and cross-camera deduplication, characterized in that: Based on multi-cue fusion and deep learning, this system performs pedestrian role recognition and cross-camera deduplication in surveillance videos, achieving end-to-end processing from video input to result output, effectively addressing the complex challenges of real-world surveillance applications. The system includes the following steps: Step 1, multi-channel video input and preprocessing; Step 2, pedestrian detection and single-camera tracking; Step 3, pedestrian feature extraction and role reasoning; Step 4, cross-camera deduplication; Step 5, global identity and role information management; Step 6, result output and application interface.

2. The engineered method for pedestrian role recognition and cross-camera deduplication according to claim 1, characterized in that: In step one, the system receives video streams in real time from multiple surveillance cameras deployed in different locations and performs necessary preprocessing on the input video streams to improve the quality of subsequent analysis. Specific preprocessing methods include decoding, frame synchronization, and image enhancement.

3. The engineered method for pedestrian role recognition and cross-camera deduplication according to claim 1, characterized in that: In step two, each preprocessed video stream is sent to a high-performance pedestrian detector module. This module uses an advanced deep learning target detection algorithm to detect pedestrian targets in real time in each frame of the video and output their bounding box coordinates and detection confidence. Next, within the field of view of each independent camera, the detected pedestrians are continuously tracked using a single-camera multi-target tracking algorithm. A continuous motion trajectory segment is generated for each pedestrian within the current camera's field of view, and a temporary camera ID is assigned to it.

4. The engineered method for pedestrian role recognition and cross-camera deduplication according to claim 1, characterized in that: In step three, for each pedestrian or their trajectory segment successfully tracked within a single camera, the system will call the multi-clue feature extractor module to extract a rich set of feature descriptors from their image sequence and motion information; The appearance features utilize deep convolutional neural networks to extract global and salient local visual appearance features of pedestrians. These features are robust to changes in lighting and viewing angle. Fine-grained semantic attributes are identified through a dedicated pedestrian attribute recognition submodule, which identifies various semantic attributes of pedestrians. Predefined roles refer to the predefined trajectory features of some roles, mainly clothing and appearance. A feature comparison library is established, and the trajectory of specific roles is determined by feature similarity comparison. Interaction feature analysis analyzes pedestrian interaction behavior in a scene, including: interaction with predefined areas, interaction with other identified objects in the scene, and social interaction with other pedestrians. These features can be used to uniformly update roles after the trajectory is re-identified to obtain a unique pedestrian ID.

5. The engineered method for pedestrian role recognition and cross-camera deduplication according to claim 4, characterized in that: In step three, after the above features are integrated, they are input into the core pedestrian role inference engine PRIE. PRIE employs a multi-layered decision engine based on business logic. According to a pre-defined role definition library and combined with real-time input multi-dimensional information, it assigns one or more most probable role labels to each observed pedestrian and provides corresponding confidence scores. All the above feature descriptors are divided into two categories: quantitative features and attribute / behavioral features. Quantitative features are... To describe, attribute behavioral characteristics are used To describe it; in addition to the two types of features mentioned above, it also retains the high-dimensional features that characterize the human body itself, using These features serve as the necessary basis for calculations, are utilized in subsequent steps, and are ultimately integrated into unified role information and pedestrian tags.

6. The engineered method for pedestrian role recognition and cross-camera deduplication according to claim 4, characterized in that: The fourth step is pedestrian re-identification. When a pedestrian appears in the field of view of a certain camera or when the trajectory segment ends, the extracted multimodal features are sent to the cross-camera deduplication module CCDM. CCDM first efficiently compares these features with a feature database of all globally unique pedestrian identities already existing in the system. The matching algorithm strategy employed aims to overcome the limitations of traditional methods. The shortcomings are that it is divided into several steps with precise semantics. For ease of description, the following variables and functions are set: Pedestrian features are composed of the features from step three: Determination of pedestrian group characteristics: ;in, Representing specific feature conditions, the returned value is... These represent conditions not being met and conditions being met, respectively; Similarity determination: ;in, Represents the similarity threshold. It's a vector similarity function; a commonly used one is cosine similarity, which returns... These represent insufficient similarity and sufficient similarity, respectively; Aggregate decision function: ;in, Represents the similarity threshold, and the returned values ​​are... These represent unaggregated and aggregated elements, respectively. Specifically, the function's decision logic is as follows: 。 7. The engineered method for pedestrian role recognition and cross-camera deduplication according to claim 6, characterized in that: Step four uses the above definitions to more clearly describe and compare each of the following clustering steps, including high similarity trajectory aggregation, high similarity aggregation among trajectories in the same direction, similarity aggregation among isolated trajectories in the same direction, similarity aggregation among trajectories in opposite directions, low similarity aggregation of isolated trajectories with the same attribute, fallback matching and recall of trajectories with special attributes, and time and space constraints; High similarity trajectory aggregation: First, using higher similarity, trajectories are initially aggregated with high confidence, initially aggregating the most similar trajectories of the same person; High similarity aggregation among trajectories in the same direction: Since trajectories of the same person in the same direction often have high similarity, under the premise of trajectories in the same direction aggregation, medium to high similarity also has high confidence; Similarity aggregation among isolated trajectories in the same direction: Generally speaking, isolated, unmatched trajectories are due to some abnormal situation. In this case, the similarity threshold can be lowered to implement medium similarity aggregation, which can significantly reduce the missed aggregation rate of trajectories in the same direction; Similarity aggregation among trajectories in opposite directions: Trajectories from different directions belonging to the same person often lack high similarity. Therefore, a medium similarity threshold is typically used when aggregating dissimilar trajectories. After this step, most trajectories have been aggregated. For isolated trajectories with the same attribute and low similarity aggregation: after the previous step, most trajectories have been successfully aggregated, leaving a small number of isolated, unaggregated trajectories. These can only be aggregated with low similarity. To avoid false aggregation, trajectory attribute features from the feature extraction and role inference stages need to be included. For special attribute trajectories with fallback matching and recall: some trajectories have special attributes. A fallback recall step is added as needed, using a relatively low threshold and more lenient conditions to ensure high recall for these trajectories. Temporal and spatial constraints: these can be added at each aggregation step. With medium or lower similarity thresholds, adding temporal and spatial constraints can significantly reduce the number of trajectory pairs requiring matching and increase aggregation accuracy.

8. The engineered method for pedestrian role recognition and cross-camera deduplication according to claim 1, characterized in that: In step five, the system maintains a central or distributed database to store and manage the globally unique IDs of all pedestrians, trajectory segments from different cameras associated with each ID, role labels assigned at each time point or time period, and various feature vectors used for identification and matching. This database supports efficient query and update operations, enabling upper-layer applications to easily retrieve the complete activity history of any individual in the monitoring network. Furthermore, to ensure data consistency, each aggregated pedestrian trajectory group needs to undergo unified label reassignment. Different reassignment strategies are adopted for two different types of feature labels, specifically: quantitative features... and attribute behavioral characteristics Quantitative characteristics Assume the first The integration threshold for each feature is Then after redistribution The tags are: ;in, For smoothing statistical functions, common choices include the mean and median, ultimately returning... These represent whether the tag is active or not; attribute behavior characteristics. After redistribution The tags are: Generally, the mode is used as the aggregation function for categorical variables. However, the mode may return multiple values. Therefore, an outer function with a unique value is needed. A priority order can be set when generating tie candidate tags according to business needs.

9. The engineered method for pedestrian role recognition and cross-camera deduplication according to claim 1, characterized in that: In step six, based on the analysis results, the system provides various forms of output to upper-layer applications and user interfaces through standardized interfaces, including real-time alarms, visualizations, data reports and dashboards, open APIs, and overall system data flow. Real-time alerts are triggered when a specific role or a combination of specific roles exhibits specific behaviors. The visualization displays pedestrian movement trajectories, bounding boxes, global IDs, and currently identified role labels overlaid on electronic maps or real-time camera footage; data reports and dashboards mainly generate various statistical reports and visualization dashboards on pedestrian flow statistics, heat maps of specific role activities, customer movement analysis, and employee work efficiency evaluation; the open API mainly provides API interfaces, allowing third-party applications or platforms to integrate and call pedestrian analysis capabilities; the entire process of the overall system data flow reflects the layer-by-layer extraction from raw data to semantic information.

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