Earticle

초록

영어

Sparse representation has been successfully applied to visual tracking to find the target with the minimum reconstruction error from the target templates subspace. Traditional sparsity-based trackers handle corruptions and occlusions of the observation by introducing a set of trivial templates. However, the performance is not so satisfactory in practice. It is because the trivial templates unable to model heavy occlusions effectively, and the likelihood computation and the template update processes do not take full advantage of the occlusion information. In this paper, we propose a novel tracking method taking advantage of local sparse representation to detect occlusions during the tracking sequence. In our method, the target is divided into local patches. We analyze the spatial distribution of the samples employed by the local sparse representation, and determine the occlusion state for each patch respectively. The occluded patches are disregard, only the unoccluded ones are considered for reconstruction and likelihood computation. In addition, a dynamic template update strategy with occlusion handling is introduced to alleviate the drift problem. Experiments on challenging video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Occlusion Detection based on Local Sparse Representation
  3.1. Local Sparse Representation
  3.2. Kernel based Occlusion Detector
 4. Proposed Tracking Algorithm
  4.1. Tracking with Occlusion Handling
  4.2. Template Update
 5. Experiments
  5.1. Qualitative Evaluation
  5.2. Quantitative Evaluation
 6. Conclusion
 References

키워드

저자

• Hainan Zhao [ Computer Application Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China ]
• Xuan Wang [ Computer Application Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China ]
• Meng Liu [ Computer Application Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605