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Akbari, Morteza, Razavi, Seyyed Mohammad, Mohamadzadeh, Sajad. (1404). Robust Continuous Person Tracking in Dense Multi-Camera Environments through Decoupled Graph Learning. فناوری آموزش, (), -. doi: 10.22061/jecei.2025.12094.853
Morteza Akbari; Seyyed Mohammad Razavi; Sajad Mohamadzadeh. "Robust Continuous Person Tracking in Dense Multi-Camera Environments through Decoupled Graph Learning". فناوری آموزش, , , 1404, -. doi: 10.22061/jecei.2025.12094.853
Akbari, Morteza, Razavi, Seyyed Mohammad, Mohamadzadeh, Sajad. (1404). 'Robust Continuous Person Tracking in Dense Multi-Camera Environments through Decoupled Graph Learning', فناوری آموزش, (), pp. -. doi: 10.22061/jecei.2025.12094.853
Akbari, Morteza, Razavi, Seyyed Mohammad, Mohamadzadeh, Sajad. Robust Continuous Person Tracking in Dense Multi-Camera Environments through Decoupled Graph Learning. فناوری آموزش, 1404; (): -. doi: 10.22061/jecei.2025.12094.853

Robust Continuous Person Tracking in Dense Multi-Camera Environments through Decoupled Graph Learning

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 25 آبان 1404
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2025.12094.853
نویسندگان
Morteza Akbari؛ Seyyed Mohammad Razavi؛ Sajad Mohamadzadeh*
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 10 تیر 1404،  تاریخ بازنگری: 19 مهر 1404،  تاریخ پذیرش: 07 آبان 1404 
چکیده
Background and Objectives: Multi-object tracking in dense, multi-camera environments remains challenging due to occlusions, lighting variations, and fragmented trajectories. While existing methods rely on hierarchical two-step approaches or complex Bayesian filters, they often fail to fully exploit spatio-temporal correlations or to approach global consistency across cameras and frames. This study aims to address these limitations by proposing a novel graph-based deep learning model for continuous person tracking that independently optimizes spatial and temporal associations.
Methods: The proposed model decomposes multi-camera tracking into two tasks: temporal association (linking objects across frames using velocity and time) and spatial association (aligning objects from multiple viewpoints). A spatio-temporal graph structure is constructed, with nodes representing detected objects and edges encoding relationships. Message Passing Networks (MPNs) iteratively update node and edge features, while a graph consensus fusion module merges spatial and temporal graphs for robust tracking. The model is trained using Focal Loss and evaluated on the Wildtrack and CAMPUS datasets.
Results: The model achieves state-of-the-art performance, with a MOTA score of 85.5% on Wildtrack and 77.4–87.4% on CAMPUS subsets. Key improvements include a 100% MT (mostly tracked) rate and 0% ML (mostly lost) rate on CAMPUS, demonstrating exceptional robustness in occluded and crowded scenes. The IDF1 score (87.2%) highlights superior identity preservation. The decoupled design reduces graph size, which improves scalability.
Conclusion: By decoupling spatial and temporal associations and leveraging graph-based optimization, the proposed model significantly enhances tracking accuracy and reliability in multi-camera settings. This work provides a framework for applications like surveillance and autonomous systems, with future potential for attention mechanisms and adaptive graph integration.
کلیدواژه‌ها
Person tracking؛ Multi-camera environment؛ Deep learning؛ Spatio-temporal features؛ graph neural networks
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