Earticle

초록

영어

Recently, the compressive tracking (CT) method has attracted much attention due to its
high efficiency. However, the CT extracts samples around the previous target region
within a fixed search radius; the searching area is unsuitable when the target undergoes
abrupt acceleration change. Meanwhile, the classifier learns the features of the target
online without judgment even the target is fully occluded. Thus, the improper searching
area and incorrectly updated features lead to a marked drop in precision of tracking. To
solve this issue, a robust target tracking method integrating spatio-temporal model to
constrain the searching area is proposed in this paper. Different from CT, the proposed
method initially constructs the spatio-temporal model to calculate a confidence map
between consecutive frames, and the region with high confidence suggests the high
possibility that target exists. Thus the samples can be extracted in the high confidence
area. Then, the optimal target location can be estimated with a naive Bayes classifier
using sparse coding features. Experiments show that the proposed method outperforms
several competing methods in efficiency and robustness.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Compressive Tracking
  3.1. Compressive Sensing
  3.2. Online Classifier Update
 4. Proposed Algorithm
  4.1. Spatial Relationship Model
  4.2. Spatio-Temporal Model
  4.3. Update Searching Area
 5. Experiments
  5.1. Experimental Setup
  5.2. Experimental Results
 6. Conclusion
 References

키워드

저자

• Min Jiang [ Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China ] Corresponding Author
• Jiao Wu [ Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China ]
• Jun Kong [ Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China / College of Electrical Engineering, Xinjiang University, Urumqi, China ]
• Chenhua Liu [ Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China ]
• Shengwei Tian [ College of Electrical Engineering, Xinjiang University, Urumqi, 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