# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 6 Conclusions  
In this work, our goal is to tackle the uncertainty problem of two-phase motion patterns when tracking pedestrians. We propose a novel multi-agent framework for MOT, leveraging multi-agent decision-making to enhance the efficiency and scalability of track management in complex scenes. In addition, the framework integrates two key methodological innovations: HMM-based reconstruction of the Kalman filter and a weighted entropy mechanism for the adaptive fusion of appearance and motion features. Quantitative evaluations on the MOT17 benchmark demonstrate significant advancements over prior methods: Our method achieves 82.1 in IDF1 (+1.9 vs. second-best), 81.5 in MOTA, and 65.9 in HOTA (+0.8 vs. second-best), establishing new state-of-the-art results in all three primary metrics. For the challenging MOT20 benchmark featuring extreme crowd densities, our method achieves 81.2 in IDF1, 78.4 in MOTA, 65.7 in HOTA, and 608 IDs--all ranking first without dataset-specific fine-tuning. And the improvement in the FPS metric demonstrates that the multi-agent approach can enhance the efficiency of computation and track management. Future work could further explore the decentralization of MOT. On the one hand, centralized computing architectures face inherent limitations in terms of security and robustness, primarily due to their susceptibility to single-point failures--a critical vulnerability that renders them unsuitable for deployment in industrial environments. On the other hand, it brings inevitable risks of data leakage for data owners and the administrator. Meanwhile, emerging blockchain technology has become a promising paradigm to enable trustworthy collaborative frameworks to be established, leveraging its salient attributes of decentralization, transparency, traceability, and immutability. The framework of blockchain-based MOT can consist of edge devices, compute units, and blockchain networks. Edge devices contain surveillance cameras and the communication protocol. Compute units include object detectors, ReID modules, and trackers. This architecture facilitates the seamless integration of multi-agent coordination mechanisms. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Method** & **Contributions and Limitations** \\ \hline DMARL-Tracker [8] & Proposes a distributed MOT algorithm based on MARL. Designs a collaborative blockchain-based MOT framework. Fixed weight parameters lack robustness. \\ \hline SparseTrack [36] & A method to obtain relative depth from 2D images. An effective depth cascade matching approach. A new IoU-only tracker with depth cascade matching. Neglects the probabilistic problems of uncertainties in MOT. \\ \hline MLT [7] & A detection multiplexing method for mutual occlusion. An MLG model using the detection multiplexing method. A near-online optimization method of solving MLG. Lacks analysis of moving patterns for pedestrians. \\ \hline StrongSORT [1] & Equips DeepSORT with advanced modules and tricks. Proposes two lightweight algorithms, AFLink and GSI. Heavy computation loads result in low FPS. \\ \hline Ours & Decomposes pedestrian locomotion into two phases. Reconstructs Kalman filters in a unified HMM framework. Proposes a weighted entropy mechanism for adaptive fusion. Introduces multi-agents to manage tracks separately. Restricted in scenarios of tracking pedestrians. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
**Author Contributions:** Resources, H.S. ; Writing--original draft, H.G. ; Writing--review & editing, S.W., D.Y. ([PERSON]), D.Y. ([PERSON]) and G.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partially funded by the National Key R&D Program of China (No. 2024 YFB4506000); the National Natural Science Foundation of China (No. 62394332, 62372023); the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2023 ZX-11); the Research Start-up Funds of Hangzhou International Innovation Institute of Beihang University (Grant No.