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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 147353, 12 pages
Research Article

Low-Rank Representation-Based Object Tracking Using Multitask Feature Learning with Joint Sparsity

Department of Image, Chung-Ang University, Seoul 156-756, Republic of Korea

Received 13 August 2014; Revised 7 November 2014; Accepted 7 November 2014; Published 23 November 2014

Academic Editor: Sergei V. Pereverzyev

Copyright © 2014 Hyuncheol Kim and Joonki Paik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We address object tracking problem as a multitask feature learning process based on low-rank representation of features with joint sparsity. We first select features with low-rank representation within a number of initial frames to obtain subspace basis. Next, the features represented by the low-rank and sparse property are learned using a modified joint sparsity-based multitask feature learning framework. Both the features and sparse errors are then optimally updated using a novel incremental alternating direction method. The low-rank minimization problem for learning multitask features can be achieved by a few sequences of efficient closed form update process. Since the proposed method attempts to perform the feature learning problem in both multitask and low-rank manner, it can not only reduce the dimension but also improve the tracking performance without drift. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods for tracking objects in challenging image sequences.