NDLI: Online Robust Non-negative Dictionary Learning for Visual Tracking

Content Provider	IEEE Xplore Digital Library
Author	Naiyan Wang Jingdong Wang Dit-Yan Yeung
Copyright Year	2013
Description	Author affiliation: Hong Kong Univ. of Sci. & Technol., Hong Kong, China (Naiyan Wang; Dit-Yan Yeung)
Abstract	This paper studies the visual tracking problem in video sequences and presents a novel robust sparse tracker under the particle filter framework. In particular, we propose an online robust non-negative dictionary learning algorithm for updating the object templates so that each learned template can capture a distinctive aspect of the tracked object. Another appealing property of this approach is that it can automatically detect and reject the occlusion and cluttered background in a principled way. In addition, we propose a new particle representation formulation using the Huber loss function. The advantage is that it can yield robust estimation without using trivial templates adopted by previous sparse trackers, leading to faster computation. We also reveal the equivalence between this new formulation and the previous one which uses trivial templates. The proposed tracker is empirically compared with state-of-the-art trackers on some challenging video sequences. Both quantitative and qualitative comparisons show that our proposed tracker is superior and more stable.
Sponsorship	IEEE Comput. Soc.
Starting Page	657
Ending Page	664
File Size	642732
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.87
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Dictionaries Visualization Video sequences Object tracking Vectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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