Contextual Mixture Tracking
Multiple object tracking (MOT) poses three challenges to conventional well-studied single object tracking (SOT) algorithms: 1) multiple targets lead the configuration space to be exponential to the number of targets; 2) multiple motion conditions due to multiple targets' entering, exiting and intersection make the prediction process degrade in precision; 3) visual ambiguities among nearby targets make the trackers error prone. In this paper, we address the MOT problem by embedding contextual proposal distributions and contextual observation models into a mixture tracker which is implemented in a Particle Filter framework. The proposal distributions are adaptively selected by motion conditions of targets which are determined by context information, and the multiple features are combined according to their discriminative power between ambiguity prone objects. The induction of contextual proposal distribution and observation model can help to surmount the incapability of conventional mixture tracker in handling object occlusions, meanwhile retain its merits of flexibility and high efficiency. The final experiments show significant improvement in variable number objects tracking scenarios compared with other methods.