Learn++ for robust object tracking

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.