Multi-Target Tracking by Learning Class-Specific and Instance-Specific Cues

This paper proposes a novel particle filtering framework for multi-target tracking by using online learned class-specific and instancespecific cues, called Data-Driven Particle Filtering (DDPF). The learned cues include an online learned geometrical model for excluding detection outliers that violate geometrical constraints, global pose estimators shared by all targets for particle refinement, and online Boosting based appearance models which select discriminative features to distinguish different individuals. Targets are clustered into two categories. Separatedtarget is tracked by an ISPF (incremental self-tuning particle filtering) tracker, in which particles are incrementally drawn and tuned to their best states by a learned global pose estimator; target-group is tracked by a joint-state particle filtering method in which occlusion reasoning is conducted. Experimental results on challenging datasets show the effectiveness and efficiency of the proposed method.