NDLI: Robust Object Tracking with Online Multi-lifespan Dictionary Learning

Content Provider	IEEE Xplore Digital Library
Author	Junliang Xing Jin Gao Bing Li Weiming Hu Shuicheng Yan
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Shuicheng Yan) \|\| Inst. of Autom., Beijing, China (Junliang Xing; Jin Gao; Bing Li; Weiming Hu)
Abstract	Recently, sparse representation has been introduced for robust object tracking. By representing the object sparsely, i.e., using only a few templates via L1-norm minimization, these so-called L1-trackers exhibit promising tracking results. In this work, we address the object template building and updating problem in these L1-tracking approaches, which has not been fully studied. We propose to perform template updating, in a new perspective, as an online incremental dictionary learning problem, which is efficiently solved through an online optimization procedure. To guarantee the robustness and adaptability of the tracking algorithm, we also propose to build a multi-lifespan dictionary model. By building target dictionaries of different life spans, effective object observations can be obtained to deal with the well-known drifting problem in tracking and thus improve the tracking accuracy. We derive effective observation models both generatively and discriminatively based on the online multi-lifespan dictionary learning model and deploy them to the Bayesian sequential estimation framework to perform tracking. The proposed approach has been extensively evaluated on ten challenging video sequences. Experimental results demonstrate the effectiveness of the online learned templates, as well as the state-of-the-art tracking performance of the proposed approach.
Sponsorship	IEEE Comput. Soc.
Starting Page	665
Ending Page	672
File Size	1554135
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.88
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	Dictionaries Target tracking Adaptation models Robustness Object tracking Superluminescent diodes Buildings dictionary learning template update sparse representation
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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