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

Hyuncheol Kim and Joonki Paik

Abstract and Applied Analysis, 2014, vol. 2014, issue 1

Abstract: 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.

Date: 2014
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https://doi.org/10.1155/2014/147353

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:147353

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