Learn++ for Robust Object Tracking

Feng Zheng, Ling Shao, James Brownjohn, Vitomir Racic

Research output: Contribution to conferencePaper

6 Citations (Scopus)


In this paper, a Learn++ (LPP) tracker is proposed to efficiently select specific classifiers for robust and long-term object tracking. In contrast to previous online methods, LPP tracker dynamically maintains a set of basic classifiers which are trained sequentially without accessing original data but preserving the previously acquired knowledge. The different subsets of basic classifiers can be specified to solve different sub-problems occurred in a non-stationary environment. Thus, an optimal classifier can be approximated in an active subspace spanned by selected adaptive basic classifiers. As a result, LPP
tracker can address the “concept drift”, by automatically adjusting the active subset and searching the optimal classifier in an active subspace spanned by the subset according to the distribution of the samples and recent performance. Experimental results show that LPP tracker yields state-of-the-art performance under various challenging environmental conditions and, especially, can overcome several challenges simultaneously.
Original languageEnglish
Publication statusPublished - 2014
Event25th British Machine Vision Conference, BMVC 2014 - Nottingham, United Kingdom
Duration: 1 Sep 20145 Sep 2014


Conference25th British Machine Vision Conference, BMVC 2014
Country/TerritoryUnited Kingdom

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