A structured learning-based graph matching for dynamic multiple object tracking
To correctly detect dynamic targets and obtain a record of the trajectories of identical targets in appearance over time, has become significantly more challenging and infers countless applications in biomedicine. In this paper, we propose a novel structured learning-based graph matching algorithm to track a variable number of interacting objects in dynamic environments. Different from previous approaches, the proposed method takes full advantage of neighboring relationships as edge feature in the structured graph. The target 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 which is solved by dynamic Hungarian algorithm. The parameters of the structured graph matching model can be acquired in a stochastic graduated learning step in different dynamic environments. The extensive experiments on dynamic cell and football sequences demonstrate that the resulting approach deals effectively with complicated target interactions.