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Road pedestrian detection and tracking algorithm based on improved YOLOv5s and DeepSORT.

Guofeng Qin1,2, Rongting Pan3,2, Yi Deng4

  • 1Teachers College for Vocational and Technical Education, Guangxi Normal University, Guilin, China.

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Summary
This summary is machine-generated.

This study introduces an improved pedestrian detection and tracking algorithm using enhanced YOLOv5s and DeepSORT. The new method significantly boosts accuracy and tracking stability, especially for small objects in dense traffic scenarios.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Pedestrian detection and tracking face challenges with low accuracy, high miss rates, and poor stability in dense, occluded, and small object scenarios on roads.
  • Existing algorithms struggle to maintain performance under these difficult conditions, necessitating improved methods.

Purpose of the Study:

  • To develop a robust pedestrian detection and tracking algorithm that overcomes limitations in accuracy, miss detection, and tracking stability.
  • To enhance performance specifically for dense occlusion and small object detection in traffic environments.

Main Methods:

  • Improved YOLOv5s detection network incorporating Focal-EIoU loss, a Small Object (SO) detection layer, and Multi-Head Self-Attention (MHSA) mechanism.
  • Enhanced DeepSORT tracking framework with a lightweight ShuffleNetV2 network for appearance feature extraction.
  • Integration of improved YOLOv5s with the modified DeepSORT for comprehensive pedestrian tracking.

Main Results:

  • Improved YOLOv5s achieved mAP0.5 of 80.8% and mAP0.5:0.95 of 49.7%, outperforming original YOLOv5s by 4.4% and 3.9%.
  • Enhanced YOLOv5s-DeepSORT achieved MOTA of 50.7% and MOTP of 77.3%, with an 11.3% reduction in identity switches.
  • Model size reduced to 20% of the original, enhancing portability without compromising accuracy.

Conclusions:

  • The proposed improved YOLOv5s-DeepSORT algorithm offers superior performance in pedestrian detection and tracking, particularly in challenging dense and small object scenarios.
  • The method demonstrates significant improvements in accuracy, tracking stability, and efficiency, making it suitable for real-world traffic applications.
  • The enhanced algorithm is robust and capable of effectively tracking targets of varying sizes, addressing key limitations of previous approaches.