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<p>This paper presents an efficient real-time person re-identification (ReID) and pedestrian tracking solution optimized for resource-constrained edge devices in multi-camera surveillance. Our key contribution is a hybrid distributed architecture that offloads lightweight detection tasks (using YOLOv10n) to edge devices, while a centralized server handles advanced feature extraction (OSNet) and robust identity tracking (ByteTrack). To improve efficiency, we integrate adaptive frame skipping on edge devices and parallel batch processing on the server. Semantic-enhanced embeddings and a memory-based retrieval mechanism improve ReID performance in crowded scenes. Additionally, we employ Apache Kafka for efficient load balancing and video stream management. Experimental results on CUHK03 and Penn-Fudan demonstrated high accuracy while maintaining real-time performance on limited-resource hardware (2 vCPU, 4 GB RAM, and Jetson Nano). These results make our approach a practical solution for real-world surveillance applications in crowded environments. Our code is available at: <a href="https://github.com/2uanDM/reid-pipeline">https://github.com/2uanDM/reid-pipeline</a>.</p>

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Real-time person re-identification and tracking on edge devices with distributed optimization

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Tuan Linh Dang,
  • Minh Hoang Hoang,
  • Viet Anh Ngo,
  • Minh Quan Duong,
  • Hoang Hiep Ha,
  • The An Nguyen,
  • Hoang Le

摘要

This paper presents an efficient real-time person re-identification (ReID) and pedestrian tracking solution optimized for resource-constrained edge devices in multi-camera surveillance. Our key contribution is a hybrid distributed architecture that offloads lightweight detection tasks (using YOLOv10n) to edge devices, while a centralized server handles advanced feature extraction (OSNet) and robust identity tracking (ByteTrack). To improve efficiency, we integrate adaptive frame skipping on edge devices and parallel batch processing on the server. Semantic-enhanced embeddings and a memory-based retrieval mechanism improve ReID performance in crowded scenes. Additionally, we employ Apache Kafka for efficient load balancing and video stream management. Experimental results on CUHK03 and Penn-Fudan demonstrated high accuracy while maintaining real-time performance on limited-resource hardware (2 vCPU, 4 GB RAM, and Jetson Nano). These results make our approach a practical solution for real-world surveillance applications in crowded environments. Our code is available at: https://github.com/2uanDM/reid-pipeline.