Enterprise AI Analysis
A Fusion Algorithm for Pedestrian Anomaly Detection and Tracking on Urban Roads Based on Multi-Module Collaboration and Cross-Frame Matching Optimization
Amid rapid advancements in artificial intelligence, the detection of abnormal human behaviors in complex traffic environments has garnered significant attention. However, detection errors frequently occur due to interference from complex backgrounds, small targets, and other factors. Therefore, this paper proposes a research methodology that integrates the anomaly detection YOLO-SGCF algorithm with the tracking BoT-SORT-ReID algorithm.
Executive Impact: Key Metrics
Our comprehensive analysis reveals that this AI solution significantly enhances detection accuracy and tracking stability for abnormal pedestrian behaviors in complex urban environments, outperforming current mainstream models across multiple key metrics while maintaining real-time processing capabilities. This advancement is crucial for improving safety and operational efficiency in dynamic public spaces.
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mAP@50%
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Precision
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Recall
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MOTA
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MOTP
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IDS Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Benefits in Object Detection
- Improved mAP@50% by 3.4% and mAP@50-95% by 8.1% over original YOLOv8n.
- Enhanced feature modeling with Swin Transformer for global context and multi-scale objects.
- Precise object localization and classification through hybrid CA+CBAM attention mechanisms.
- Reduced computational load and improved efficiency with GSConv modules.
- More accurate bounding box regression and sample imbalance handling via Focal-EIoU loss.
Key Benefits in Object Tracking
- Achieved 90.8% MOTA and 92.6% MOTP, outperforming mainstream algorithms.
- Reduced Identity Switches (IDS) to 11, a 47.6% reduction, ensuring high identity consistency.
- Robust tracking in complex scenes by integrating ReID features and motion compensation (BoT-SORT-ReID).
- Maintains near real-time performance (20 FPS) suitable for practical applications.
Key Benefits in Multi-Module Fusion
- Synergistic improvements across detection and tracking, combining YOLO-SGCF and BoT-SORT-ReID.
- Dynamic calibration and optimization of detection results with tracking trajectories.
- Enhanced generalization capability, achieving 92.7% mAP on UCSD Ped1 dataset.
- A holistic solution for real-time automated early warning in urban traffic environments.
Overall Detection Accuracy
92.2% mAP@50% on Custom DatasetTracking Performance
90.8% Multi-Object Tracking Accuracy (MOTA)Enterprise Process Flow
Input Video Sequence
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YOLO-SGCF Detection Module
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BoT-SORT-ReID Tracking Module
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Cross-Module Information Coordination
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Output Abnormal Behavior & Trajectories
| Feature | Our Model (YOLO-SGCF & BoT-SORT-ReID) | Mainstream Alternatives |
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| Detection mAP@50% |
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| Tracking MOTA |
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| Identity Switches (IDS) |
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| Generalization (UCSD Ped1 mAP) |
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Impact on Urban Road Safety
Our fusion algorithm has been rigorously tested on diverse urban road scenarios, successfully identifying and tracking critical abnormal pedestrian behaviors like Fall, Fight, Climb, and Phone use. This technology provides real-time insights, enhancing public safety and enabling proactive intervention in complex traffic environments.
Advanced ROI Calculator
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Estimated Annual Savings
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Implementation Roadmap
Our structured approach ensures a smooth, efficient, and impactful AI integration into your enterprise.
Phase 1: Initial System Integration & Data Preparation
Integrate detection and tracking modules. Prepare and preprocess your enterprise's specific video datasets for training and validation.
Phase 2: Model Adaptation & Fine-tuning
Fine-tune the YOLO-SGCF and BoT-SORT-ReID models using your custom data, adapting them to specific environmental conditions and abnormal behavior patterns.
Phase 3: Pilot Deployment & Real-time Evaluation
Deploy the integrated system in a pilot environment. Conduct real-time evaluations to assess performance, stability, and identify areas for optimization under operational conditions.
Phase 4: Scalable Integration & Continuous Optimization
Scale the solution across your infrastructure. Establish continuous monitoring, feedback loops, and model updates to ensure long-term effectiveness and adaptability to evolving scenarios.
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