Tracking Game: Self-adaptative Agent based Multi-object Tracking

Multi-object tracking (MOT) has become a hot task in multi-media analysis. It not only locates the objects but also maintains their unique identities. However, previous methods encounter tracking failures in complex scenes, since they lose most of the unique attributes of each target. In this paper, we formulate the MOT problem as Tracking Game and propose a Self-adaptative Agent Tracker (SAT) framework to solve this problem. The roles in Tracking Game are divided into two classes including the agent player and the game organizer. The organizer controls the game and optimizes the agents' actions from a global perspective. The agent encodes the attributes of targets and selects action dynamically. For these purposes, we design the State Transition Net to update the agent state and the Action Decision Net to implement the flexible tracking strategy for each agent. Finally, we present the organizer-agent coordination tracking algorithm to leverage both global and individual information. The experiments show that the proposed SAT achieves the state-of-the-art performance on both MOT17 and MOT20 benchmarks.