A joint appearance model of SRC and MFH for multi-objects tracking

This paper extends sparse representation based classification (SRC) and multi-feature hashing (MFH) into multi-object tracking task, and proposes a joint appearance model of SRC and MFH, which aims at discriminating different objects effectively. Unlike most previous approaches which only focus on producing appearance models for all targets, we further consider discriminative features for distinguishing difficult pairs of targets. Firstly, an SRC based global discriminative appearance model is designed for discriminating all targets. It formulates tracklets association as an SRC problem. A discriminative dictionary learning approach is introduced, which improves the SRC classification performance. By this way, the global discriminative appearance model can distinguish different targets more effectively. Secondly, an MFH based pairwise appearance model is designed. This pairwise appearance model focuses on distinguishable features from two targets without considering other targets or backgrounds, therefore it is more effective for differentiating specific close-by tracklets pairs. Data association framework is employed to generate final tracks. Considerable performance improvements are shown on challenging data sets, particularly in metrics of identity switches.