Abstract. Data association is an essential component of any human tracking system. The majority of current methods, such as bipartite matching, incorporate a limited-temporal-locality of the sequence into the data association problem, which makes them inherently prone to IDswitches and difficulties caused by long-term occlusion, cluttered background, and crowded scenes. We propose an approach to data association which incorporates both motion and appearance in a global manner. Unlike limited-temporal-locality methods which incorporate a few frames into the data association problem, we incorporate the whole temporal span and solve the data association problem for one object at a time, while implicitly incorporating the rest of the objects. In order to achieve this, we utilize Generalized Minimum Clique Graphs to solve the optimization problem of our data association method. Our proposed method yields a better formulated approach to data association which is supported by our superior results. Experiments show the proposed method makes significant improvements in tracking in the diverse sequences of
In order to achieve long-term data association, generalized minimum clique graph is proposed in REF to incorporate long-term temporal span.
GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs
In ECCV
Computer Science