Learn++ for robust object tracking

Zheng, Feng, Shao, Ling, Brownjohn, James and Racic, Vitomir (2014) Learn++ for robust object tracking. In: BMVC 2014 - 25th British Machine Vision Conference, 1st - 5th September 2014, Nottingham, UK.

Full text not available from this repository. (Request a copy)
Official URL: http://www.bmva.org/bmvc/2014/files/paper043.pdf


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.

Item Type: Conference or Workshop Item (Paper)
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Users 6424 not found.
Date Deposited: 16 Jun 2015 09:57
Last Modified: 13 Oct 2019 00:37
URI: http://nrl.northumbria.ac.uk/id/eprint/22929

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics