Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning

This paper focuses on the problem of tracking multiple humans in dense environments which is very challenging due to recurring occlusions between different humans. To cope with the difficulties it presents, an offline boosted multi-view upper-body detector is used to automatically initialize a new human trajectory and is capable of dealing with partial human occlusions. What is more, an online learning process is proposed to learn discriminative human observations, including discriminative interest points and color patches, to effectively track each human when even more occlusions occur. The offline and online observation models are neatly integrated into the particle filter framework to robustly track multiple highly interactive humans. Experiments results on CAVIAR dataset as well as many other challenging real-world cases demonstrate the effectiveness of the proposed method.