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Multi-target tracking by online learning of non-linear motion patterns and robust appearance models

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2 Author(s)
Bo Yang ; Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA 90089, USA ; Ram Nevatia

We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous approaches that use linear motion methods only, we online build a non-linear motion map to better explain direction changes and produce more robust motion affinities between tracklets. Moreover, based on the incremental learned entry/exit map, a multiple instance learning method is devised to produce strong appearance models for tracking; positive sample pairs are collected from different track-lets so that training samples have high diversity. Finally, using online learned moving groups, a tracklet completion process is introduced to deal with tracklets not reaching entry/exit points. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on

Date of Conference:

16-21 June 2012