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Online Learned Discriminative Part-Based Appearance Models for Multi-human Tracking
Bo Yang and Ram Nevatia
Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA 90089, USAyangbo@usc.edu
nevatia@usc.edu
Abstract. We introduce an online learning approach to produce discriminative part-based appearance models (DPAMs) for tracking multiple humans in real scenes by incorporating association based and category free tracking methods. Detection responses are gradually associated into tracklets in multiple levels to produce final tracks. Unlike most previous multi-target tracking approaches which do not explicitly consider occlusions in appearance modeling, we introduce a part based model that explicitly finds unoccluded parts by occlusion reasoning in each frame, so that occluded parts are removed in appearance modeling. Then DPAMs for each tracklet is online learned to distinguish a tracklet with others as well as the background, and is further used in a conservative category free tracking approach to partially overcome the missed detection problem as well as to reduce difficulties in tracklet associations under long gaps. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.
Keywords: multi-human tracking, online learned discriminative models
LNCS 7572, p. 484 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012