Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking

Xiangyuan LAN, Andy J. Ma, Pong Chi YUEN, Rama Chellappa

Research output: Contribution to journalArticlepeer-review

182 Citations (Scopus)

Abstract

Visual tracking using multiple features has been proved as a robust approach because features could complement each other. Since different types of variations such as illumination, occlusion, and pose may occur in a video sequence, especially long sequence videos, how to properly select and fuse appropriate features has become one of the key problems in this approach. To address this issue, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. In order to capture the non-linear similarity of features, we extend the proposed method into a general kernelized framework, which is able to perform feature fusion on various kernel spaces. As a result, robust tracking performance is obtained. Both the qualitative and quantitative experimental results on publicly available videos show that the proposed method outperforms both sparse representation-based and fusion based-trackers.

Original languageEnglish
Article number7274352
Pages (from-to)5826-5841
Number of pages16
JournalIEEE Transactions on Image Processing
Volume24
Issue number12
DOIs
Publication statusPublished - Dec 2015

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • feature fusion
  • joint sparse representation
  • Visual tracking

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