NDLI: Sparse Feature Learning for Visual Tracking by Least Absolute Shrinkage and Selection Operator

Content Provider	IEEE Xplore Digital Library
Author	Minglei Tong Junchi Yan Huanxi Liu Hong Han
Copyright Year	2011
Abstract	In this paper, a robust visual tracking method is proposed by using Least Absolute Shrinkage and Selection Operator (Lasso) in a particle filter framework. First, to locate the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by sampling particle images and sparsity vector. The lasso can replace the sparse approximation problem by a convex problem. Then, the likelihood is evaluated by the sparsity coefficients which is very different from the current tracking scheme using sparse representation. The combination of sampling images with coefficients bigger than a threshold will be taken as the tracking target without any template. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. The dynamic target updating scheme keeps track of the most representative particles throughout the tracking procedure. The proposed approach shows excellent performance on several image sequences.
Starting Page	766
Ending Page	770
File Size	624589
Page Count	5
File Format	PDF
ISBN	9781457715600
DOI	10.1109/ICIG.2011.67
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Visualization Target tracking Conferences Lighting sparse approximation Robustness particle filter tracking
Content Type	Text
Resource Type	Article

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