A Structured Learning-Based Graph Matching Method for Tracking Dynamic Multiple Objects

Detecting multiple targets and obtaining a record of trajectories of identical targets that interact mutually infer countless applications in a large number of fields. However, it presents a significant challenge to the technology of object tracking. This paper describes a novel structured learning-based graph matching approach to track a variable number of interacting objects in complicated environments. Different from previous approaches, the proposed method takes full advantage of neighboring relationships as the edge feature in a structured graph, which performs better than using the node feature only. Therefore, a structured graph matching model is established, and the problem is regarded as structured node and edge matching between graphs generated from successive frames. In essence, it is formulated as the maximum weighted bipartite matching problem to be solved using the dynamic Hungarian algorithm, which is applicable to optimally solving the assignment problem in situations with changing edge costs or weights. In the proposed graph matching model, the parameters of the structured graph matching model are determined in a stochastic learning process. In order to improve the tracking performance, bilateral tracking is also used. Finally, extensive experimental results on Dynamic Cell, Football, and Car sequences demonstrate that the new approach effectively deals with complicated target interactions.