Earticle

초록

영어

Sparse representation (compressive sampling) has achieved impressive results in object tracking by looking for the best candidate with minimum reconstruction error using the target template. However, it may fail in some circumstances such as illumination changes, scale changes, the object color is similar with the surrounding region, and occlusion etc., in addition, high computational cost is required due to numerous calculations for solving an l1 norm related minimization problems. In order to resolve above problems, a novel method is introduced by exploiting an accelerated proximal gradient approach which aims to make the tracker runs in real time; moreover, both classic principal component analysis algorithm and sparse representation schemes are adapted for learning effective observation model and reduces the influence of appearance change. Both qualitative and quantitative evaluation demonstrate that the proposed tracking algorithm has favorably better performance than several state-of-the-art trackers using challenging benchmark image sequences, and significantly reduces the computing cost.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Introduction to Sparse Prototypes
 4. Real Time Object Tracking
 5. Experiments
  5.1 Experimental settings
  5.2 Qualitative Comparison with Other Methods
  5.3. Quantitative Comparison with Other Methods
  5.4. Computational Complexity
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Dongxu Gao [ School of Information and Control Engineering,Liaoning Shihua University,China ]
• Jiangtao Cao [ School of Information and Control Engineering,Liaoning Shihua University,China ]
• Zhaojie Ju [ School of Computing, University of Portsmouth,UK ]
• Xiaofei Ji [ School of Automation, Shenyang Aerospace University, 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