Blockchain-Based Distributed Multiagent Reinforcement Learning for Collaborative Multiobject Tracking Framework

Abstract

With the development of smart cities, video surveillance has become more prevalent in urban areas. The rapid growth of data brings challenges to video processing and analysis. Multi-object tracking (MOT), one of the most fundamental tasks in computer vision, has a wide range of applications and development prospects. MOT aims to locate multiple objects and maintain their unique identities by analyzing the video frame by frame. Most existing MOT frameworks are deployed in centralized systems, which are convenient for management but have problems such as weak algorithm adaptability, limited system scalability, and poor data security. In this paper, we propose a distributed MOT algorithm based on multi-agent reinforcement learning (DMARL-Tracker), which formulates MOT as a Markov decision process (MDP). Each object adjusts its tracking strategy during interactions with the environment. The benchmark results on MOT17 and MOT20 prove that our proposed algorithm achieves state-of-the-art (SOTA) performance. Based on this, we further integrate DMARL-Tracker into the blockchain and propose a blockchain-based collaborative MOT framework. All nodes collaborate and share information through the blockchain, achieving adaptation in different complex scenarios while ensuring data security. The simulation results show that our framework achieves good performance in terms of tracking and resource consumption.