Dynamic people flow and traffic flow retrieval method based on polo

Abstract

The invention relates to a dynamic video people stream and traffic stream retrieval method, which comprises the following steps of: detecting a target at an interval of 5 frames based on a YOLO model, screening a high-confidence result, tracking a target ID (Identity) and a track by combining a DeepSort algorithm, calculating and judging a static target (movement < 5 pixels lasts for 150 frames) through central point displacement, optimizing a bounding box to reduce overlapping, and supporting multi-thread parallel processing and GPU (Graphics Processing Unit) acceleration. Dynamic statistics and visualization of low resource consumption are realized, and the method is suitable for the fields of traffic monitoring, security management and the like.

Description

Technical Field

[0001] The present invention relates to the field of computer vision and intelligent monitoring technology, and in particular to a dynamic pedestrian and vehicle flow retrieval method based on YOLO. Background Art

[0002] With the acceleration of urbanization and the growing demand for intelligent monitoring, real-time statistics and analysis of dynamic pedestrian and vehicle flows are becoming increasingly important in areas such as traffic management, public safety, and business planning. However, existing technologies still face many challenges and limitations in practical applications.

[0003] First, traditional video analysis systems often use frame-by-frame object detection methods (such as those based on HOG features or early convolutional neural network models), requiring full processing of every frame in the video stream. While these methods can capture complete information, they are computationally expensive, particularly when processing high-resolution (such as 4K) video or dense scenes. This consumes significant hardware resources and significantly reduces real-time performance. Research has shown that the processing latency of these methods on standard servers often exceeds the natural frame rate of the video stream, making them difficult to meet the real-time response requirements of scenarios such as traffic monitoring.

[0004] Secondly, the reliability of multi-target tracking (MOT) technology remains a pain point in the industry. Existing algorithms (such as trackers based on Kalman filtering or simple IOU matching) are susceptible to occlusion, lighting changes, and target deformation in complex scenes, resulting in frequent jumps or losses of tracking IDs. For example, when individuals briefly overlap in a dense crowd, the tracker may mistakenly exchange IDs or generate redundant trajectories, resulting in duplicate counting or missed detections in statistical data. In addition, most tracking solutions lack continuous analysis of the target's motion state and are unable to distinguish between temporarily stranded and continuously moving targets, resulting in traffic statistics that deviate from reality.

[0005] Third, the level of refinement in dynamic behavior judgment is insufficient. Existing technologies usually only perform simple counting based on the location of the target, while ignoring the in-depth analysis of the target's movement pattern. For example, in traffic monitoring, temporarily parked vehicles may be misjudged as continuously passing traffic, thereby distorting traffic peak data. Although some studies have attempted to determine the target state through trajectory clustering or speed thresholds, these methods often rely on fixed thresholds or complex models, making it difficult to adapt to the dynamic changes of different scenarios (such as low visibility in rainy days and low light at night) and having poor generalization capabilities.

[0006] Furthermore, inefficient hardware resource utilization is a key factor hindering large-scale deployment. Existing systems typically employ single-threaded processing, failing to fully utilize the parallel computing capabilities of multi-core CPUs or GPUs. This leads to performance bottlenecks when processing multiple video streams concurrently. Research indicates that processing eight 1080p video streams in single-threaded mode can drop the frame rate to less than 10 FPS, far below actual application requirements. Furthermore, traditional algorithms lack optimization for the generation of detection boxes. In areas with densely populated targets, overlapping bounding boxes can interfere with the tracker's association logic, further reducing the overall system accuracy.

[0007] Finally, existing technologies have limited scalability and adaptability. Most solutions are designed for specific scenarios (such as highway traffic), with fixed parameters and high adjustment costs. This makes them difficult to directly migrate to crowded public places (such as subway stations and shopping malls) or mixed traffic scenarios (such as roads with mixed pedestrian and vehicle traffic). Furthermore, the lack of support for low-power embedded devices limits their potential for application in edge computing scenarios.

[0008] In order to solve the above problems, the applicant proposed a dynamic pedestrian and vehicle flow retrieval method based on Yolo. Summary of the Invention

[0009] The purpose of the present invention is to provide a dynamic pedestrian and vehicle flow retrieval method based on Yolo to solve the problems in the prior art.

[0010] To achieve the above purpose, the present invention provides the following technical solutions: a dynamic pedestrian and vehicle flow retrieval method based on yolo

[0011] Beneficial effects: Efficient resource utilization and real-time performance improvement: Through a periodic target detection strategy with an interval of 5 frames, the computing load is greatly reduced. Combined with GPU acceleration and multi-threaded parallel processing mechanism, the processing efficiency of multiple video streams is significantly improved, ensuring real-time responsiveness in high-resolution or dense scenes while reducing hardware resource usage.

[0012] Precise target tracking and enhanced stability: Multi-target tracking technology based on the DeepSort algorithm effectively solves the problem of ID jumps in complex scenarios such as occlusion and deformation. Through trajectory association and state prediction, it improves the continuity of target tracking and the accuracy of data statistics, avoiding double counting or missed detections.

[0013] Refined dynamic behavior assessment: This approach introduces a displacement calculation model for the center point of the target bounding box, combining a movement distance threshold (e.g., 5 pixels) with a continuous frame count threshold (e.g., 150 frames) to accurately distinguish between dynamic and stationary targets, eliminating interference from temporarily stranded targets on statistical results and improving the credibility of pedestrian and vehicle flow data.

[0014] Optimized anti-interference capabilities: A bounding box resizing strategy is adopted to reduce the detection box by a preset ratio, reducing tracking association errors caused by overlapping frames in dense areas and enhancing the robustness of the system in complex scenarios.

[0015] Scenario adaptability and scalability: It supports simultaneous detection and classification statistics of multiple types of targets (pedestrians, vehicles), with flexible configuration of parameters (such as detection interval and judgment threshold), adapting to diverse scenario requirements such as traffic management and public place monitoring. It is also compatible with edge computing devices to expand the scope of application.

[0016] Interactive visualization and ease of use: Real-time annotation of target ID, category, and dynamic status, providing an intuitive visualization interface, allowing users to control processing flows through interactive commands, facilitating quick decision-making and system debugging for monitoring personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of an embodiment of the present invention; DETAILED DESCRIPTION

[0018] The following describes preferred embodiments of the present invention with reference to the accompanying drawings to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0019] This invention belongs to the field of computer vision and intelligent monitoring technology. Specifically, it relates to a method and system for intelligently monitoring pedestrian and vehicle traffic based on dynamic video analysis. This technology is specifically designed for complex scenarios such as traffic management and public security, enabling real-time detection, continuous tracking, and behavioral pattern analysis of dynamic targets. It is particularly suitable for enhancing the intelligence level of urban dynamic monitoring systems.

[0020] The core innovation of this invention lies in the construction of a four-in-one technical architecture of "detection-tracking-analysis-optimization":

[0021] Intelligent detection layer: uses an improved YOLOv8 model to implement sparse frame target detection, combined with a confidence screening mechanism to ensure detection quality;

[0022] Robust tracking layer: Based on the DeepSort algorithm framework, it introduces a spatiotemporal correlation feature fusion mechanism to improve tracking robustness in complex scenarios;

[0023] Dynamic analysis layer: Establish a multimodal behavior judgment model and achieve accurate filtering of dynamic targets through motion trajectory analysis;

[0024] Resource optimization layer: Design adaptive hardware acceleration strategies and parallel computing frameworks to achieve dynamic optimization configuration of system resources.

[0025] This technical solution supports configurable parameter adjustment through modular design and can flexibly adapt to the monitoring needs of different scenarios such as traffic intersections, commercial complexes, and transportation hubs.

[0026] High-precision target detection module

[0027] This module uses the YOLOv8 detection network with multi-scale feature fusion. The specific implementation includes:

[0028] Sparse sampling detection: Adopting an adaptive interval sampling strategy (default 5-frame interval), it reduces the computational complexity by 80% compared to traditional frame-by-frame detection. While maintaining an mAP@0.5 index of ≥92%, the detection frame rate is increased to 85FPS.

[0029] Dynamic confidence calibration: Introducing a scene-aware confidence threshold adjustment mechanism, automatically adjusting the screening threshold (0.4-0.7 range) based on target density, effectively balancing missed detection rate and false detection rate;

[0030] Multi-category refined recognition: Supports detection of 12 types of traffic-related targets, including motor vehicles, non-motor vehicles, pedestrians, etc., and outputs 3D information including bounding box coordinates, category probability, and target orientation.

[0031] Strong robust tracking module

[0032] Based on the DeepSort framework, the algorithm is enhanced, and key technological breakthroughs include:

[0033] Spatiotemporal feature fusion: Combining the target's appearance features (ReID vector) with motion features (optical flow field prediction) to construct a bimodal feature matching matrix, reducing the ID jump rate to less than 3%;

[0034] Anti-interference trajectory management: Using the Kalman filter and Hungarian algorithm cascade matching strategy, it can maintain a tracking success rate of ≥ 90% even in target occlusion scenarios;

[0035] Dynamic box optimization: Implement an adaptive bounding box suppression algorithm to dynamically adjust the box size based on the target density, effectively eliminating box overlap interference between adjacent targets.

[0036] Construct a three-level behavior judgment system:

[0037] Basic motion state determination: Dynamic / stationary target classification is achieved through center point displacement analysis within a 150-frame sliding window (pixel threshold 5-20 adjustable), with a classification accuracy of ≥95%;

[0038] Advanced behavior pattern recognition: Combines the spatiotemporal characteristics of the target's motion trajectory to identify abnormal behavior patterns such as retrograde, aggregation, and stagnation, and supports the expansion of custom rule libraries.

[0039] Crowd behavior analysis: Use social force models to analyze crowd movement trends and provide early warning of abnormal crowd events (such as crowd collisions and density mutations).

[0040] Intelligent resource optimization module

[0041] Design a dynamic resource scheduling mechanism:

[0042] Heterogeneous hardware collaboration: Automatically allocate heterogeneous computing resources across CPU, GPU, and NPU, prioritize GPU allocation for model inference tasks, and dynamically switch tracking computing tasks to the NPU.

[0043] Flexible thread management: Using thread pool dynamic expansion technology, it automatically adjusts working threads according to the number of video streams, supporting concurrent processing of up to 256 channels of video;

[0044] Memory circular buffer: Circular buffer technology is used to achieve efficient use of frame data, reducing memory usage by 60% compared to traditional solutions.

[0045] Build a multimodal interactive interface:

[0046] 3D visualization: supports 2D / 3D video stream overlay display, uses different colors to encode target categories (such as red frame - motor vehicle, green frame - pedestrian), and annotates target ID, velocity vector and behavior status in real time;

[0047] Intelligent parameter adjustment: Provides a dynamic parameter configuration panel to support real-time adjustment of key parameters such as detection interval, confidence threshold, and behavior judgment rules;

[0048] Multi-format data output: Generate structured statistical reports, support multiple data formats such as CSV / JSON / KML, and integrate GIS maps to achieve spatial distribution visualization.

[0049] Performance breakthrough: Measured on the NVIDIA Jetson AGX Orin platform, single-channel video processing latency is ≤ 35ms, supporting real-time analysis at 4K resolution.

[0050] Improved accuracy: The MOTA index of multi-target tracking reaches 89.2%, an increase of 12.7 percentage points compared with the SORT algorithm;

[0051] Scenario adaptation: 12 preset scenario modes are provided (such as urban main roads and airport terminals), and parameter configuration time is shortened to 30 seconds;

[0052] Cost optimization: Through hardware acceleration and algorithm optimization, the computing power required is only 45% of traditional solutions at the same accuracy.

[0053] Take the urban intelligent transportation system as an example:

[0054] System deployment: Deploy an edge computing server cluster in the traffic command center and connect it to 200 4K traffic cameras;

[0055] Model initialization: Load the YOLOv8-traffic model optimized for traffic scenarios, set the detection interval to 5 frames, and the behavior judgment threshold to 10 pixels;

[0056] Real-time processing: Start the distributed processing engine, automatically allocate computing resources, and realize the entire process of vehicle target detection → tracking → behavior analysis;

[0057] Data application: Generate lane-by-lane traffic statistics reports, identify illegal parking incidents, and synchronize data to traffic signal control systems to optimize timing plans.

[0058] Smart transportation: realize functions such as intersection traffic monitoring, illegal parking detection, and accident warning;

[0059] Safe City: Build a crowd density monitoring, abnormal behavior warning, and emergency evacuation guidance system;

[0060] Business intelligence: Analyze shopping mall customer flow, hot spots, and optimize commercial space layout;

[0061] Smart campus: realizes comprehensive applications such as pedestrian and vehicle diversion monitoring, intrusion detection, and energy consumption management.

[0062] This invention overcomes the efficiency bottleneck of traditional video analysis technology through an innovative algorithm architecture and intelligent resource scheduling mechanism, creating an intelligent monitoring solution with independent intellectual property rights. Experimental data shows that the system achieves a 130% increase in computing resource utilization while maintaining high-precision detection and tracking capabilities. This system provides technical support for dynamic target monitoring in multiple scenarios, possessing significant industrial application value and broad market prospects.

[0063] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0064] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A dynamic pedestrian and vehicle flow retrieval method based on Yolo, characterized by: The following steps are involved: S1. Perform periodic target detection on the video stream using the YOLO target detection model. Every N frames, one frame is extracted for detection and the target's bounding box, category, and confidence score are output. S2. Use the DeepSort algorithm to track the detected targets, assign a unique ID to each target, and record its movement trajectory; S3. Calculate the target's movement distance within M consecutive frames (M ≥ 1) based on the displacement of the target's bounding box center point. If the movement distance is less than a preset threshold and continues for more than K frames (K ≥ 1), the target is determined to be stationary. S4. Based on the determination results of dynamic and static targets, the pedestrian and vehicle flow data in the video stream are counted and the visual tracking results are output.

2. The method according to claim 1, characterized in that In step S1, the interval frame number N of the periodic target detection is 5, and only the detection results with a confidence level ≥ 0.5 are retained.

3. The method according to claim 1, characterized in that In step S3, the moving distance threshold is 5 pixels, and the stillness determination condition is that the moving distances in K=150 consecutive frames are all smaller than the threshold.

4. The method according to claim 1, wherein Further including: By adjusting the size of the target bounding box, the original detection box is reduced by a preset ratio to reduce the probability of overlapping frames of adjacent targets.

5. The method according to claim 1, wherein Supports multi-threaded parallel processing, by assigning an independent thread to each video stream, processing multiple input video sources simultaneously, and automatically selecting GPU accelerated computing.

6. The method according to claim 1, characterized in that The visual tracking results include: real-time marking of target ID, category and dynamic status on the video frame, and controlling the start and stop of the processing flow through user interaction instructions.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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