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CVPR
2012
IEEE

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

11 years 6 months ago
Multi-target tracking by online learning of non-linear motion patterns and robust appearance models
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 tracklets 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.
Bo Yang, Ram Nevatia
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Bo Yang, Ram Nevatia
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