Leveraging Future Trajectory Prediction for Multi-Camera People Tracking

Artificial intelligence-based surveillance system, one of the essential systems for smart cities, plays a critical role in ensuring the safety and well-being of individuals. In this paper, we propose a real-time, low-computation cost Multi-Camera Multi-Target (MCMT) tracking system for people, leveraging deep-learning-based trajectory prediction with spatial-temporal information and social information. By predicting people's future trajectories, our algorithm effectively handles object occlusion problems and maintains accurate tracking while keeping computational costs low. Our approach addresses object occlusion without relying on computationally expensive re-identification, and improves MCMT tracking performance using graph-based tracklet representation, and spectral clustering. As a result, our proposed approach is tested on the 2023 AI City Challenge Track 1 test dataset, automatically generated on the NVIDIA Omiverse Platform, our method achieves an IDF1 score of 0.6171 and real-time performance at 27.6 FPS. Code and pre-trained models are publicly available at https://github.com/yuntaeJ/SCIT-MCMT-Tracking.