Earticle

초록

영어

In this paper, we present a robust and fast tracking method based on joint collaborative representation. Traditional sparse coding based tracking methods code the candidates as a sparse linear combination of a series of object and trivial templates and perform time consuming L1 regularizations. In contrast to these methods, this paper adopts the L2-regularized least square models to reduce the computational complexity. The tracked object can be represented by the linear combination of a series of object templates, and also can be represented by candidate samples in the current frame. We propose a joint objective function to handle the tracking process. In addition, we introduce an effective update scheme to deal with the change of target appearance over time. Experiments on several challenging image sequences show that our proposed tracking method is robust and efficient.

목차

Abstract
 1. Introduction
 2. Motivation of This Work
  2.1. Sparse Representation or Collaborative Representation
  2.2. Object Templates or PCA Basis Vectors
 3. Joint Collaborative Representation Model
 4. Update Framework
 5. Experiments
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Fei Zhou [ Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunications ]
• Guizong Zhang [ Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunications ]
• Xinyue Fan [ Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunications ]
• Dandan Yi [ Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunications ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605