Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy

Multi-object tracking (MOT) has witnessed significant advancements in recent years, yet it remains challenged by complex uncertainties arising from pedestrian movement patterns. To address this, we present a unified framework that explicitly models pedestrian dynamics through a dual-phase paradigm, combining a Hidden Markov Model (HMM) for motion modeling and weighted entropy for adaptive multi-cue fusion. Furthermore, a multi-agent architecture is employed for track management, enabling parallelized state estimation and seamless integration of the HMM-based Kalman filter with multi-cue fusion. Quantitative evaluations show that our method achieves 82.1 in IDF1, 81.5 in MOTA, 65.9 in HOTA, and 1,255 IDs on the MOT17 benchmark, and achieves 81.2 in IDF1, 78.4 in MOTA, 65.7 in HOTA, and 608 IDs on the MOT20 benchmark, and the application of the multi-agent mechanism significantly improves the scores on FPS as a result of efficient computation. The experimental results demonstrate that the proposed method achieves state-of-the-art performance, particularly in highly crowded scenes.