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, 1-12

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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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:147353

DOI: 10.1155/2014/147353

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