International Journal of Computer Techniques Volume 12 Issue 3 | Multi-Camera Framework for Object Path Tracking and Analysis
Multi-Camera Framework for Object Path Tracking and Analysis
International Journal of Computer Techniques – Volume 12 Issue 3, May – June – 2025 | ISSN: 2394-2231
Abstract
Multi-camera object tracking (MC-OT) is a crucial component in fields like surveillance, autonomous driving, and sports analysis, ensuring continuous tracking across multiple cameras. Unlike single-camera tracking, MC-OT faces challenges such as occlusions, lighting variations, differing viewpoints, and efficient data association across views. Despite advancements through deep learning and computer vision, issues like real-time processing limitations and occlusion handling persist. This paper reviews MC-OT models and methodologies, highlighting their strengths, weaknesses, and future research directions.
Keywords
Computer Vision, Object Tracking, Multi-camera Tracking, Object Detection & Re-identification, Real-time Object Tracking
Conclusion
Advancements in multi-camera object tracking have enhanced applications such as surveillance and autonomous driving. However, challenges remain, including occlusion handling, computational efficiency, and identity consistency across cameras. Future research should focus on explainable AI, scalable models, and improving identity preservation across multiple viewpoints.
References
- Liu, W., Camps, O. I., & Sznaier, M. (2017). Multi-camera Multi-Object tracking. arXiv. https://doi.org/10.48550/arxiv.1709.07065
- Kalake, L., Wan, W., & Hou, L. (2021). Analysis of deep learning approaches in Real-Time Multi-Object Tracking. IEEE Access. https://doi.org/10.1109/access.2021.3060821
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