Multi-object tracking via discriminative appearance modeling

Tracking multiple objects is important for automatic video content analysis and virtual reality. Recently, how to formulate data association optimization more effectively to overcome ambiguous detected responses and how to build more effective association affinity model have attracted more concerns. To address these issues, we propose a metric learning and multi-cue fusion based hierarchical multiple hypotheses tracking method (MHMHT), which conducts data association more robustly and incorporates more temporal context information. The association appearance similarity is calculated using the distances between feature vectors in each associated tracklet and the salient templates of each track hypothesis, which is then fused with the dynamic similarity calculated according to Kalman filter online to get association affinity. To make appearance similarity more discriminative, the spatial-temporal relationships of reliable tracklets in sliding temporal window are used as constraints to learn the discriminative appearance metric which measures the distance between feature vectors and salient templates. The salient templates of generated track hypotheses are updated using an incremental clustering method, considering the high order temporal context information. We evaluate our MHMHT tracker on challenging benchmark datasets. Qualitative and quantitative evaluations demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.