Earticle

초록

영어

Features for representing the target are the fundamental ingredient when constructing the appearance model in the tracking problem. Only one type of features is utilized to represent the target in most current algorithms. However, the limited representation of a single feature might not resist all appearance changes of the target during the tracking process. To cope with this problem, we propose a novel tracking algorithm - Mean Kernel Tracker (MKT) - to robustly locate the object. The MKT combines three complementary features - Color, HOG (Histogram of Oriented Gradient) and LBP (Local Binary Pattern) - to represent the target. And Extensive experiments on public benchmark sequences show MKT performs favorably against several state-of-the-art algorithms.

목차

Abstract
 1. Introduction
 2. Mean Kernel Learning
 3. Details of the Implementation
  3.1. Preparation of Training Sets
  3.2. The Features for Tracking
  3.3. Parameter Settings
 4. Experiments
 5. Conclusion
 References

키워드

저자

• Lei Li [ School of Technology, Beijing Forestry University, 100083, Beijing, China, Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029, Beijing, China ]
• Ruiting Zhang [ Canvard College, Beijing Technology and Business University, 101118, Beijing, China ]
• Jiangming Kan [ School of Technology, Beijing Forestry University, 100083, Beijing, China ] Corresponding Author
• Wenbin Li [ School of Technology, Beijing Forestry University, 100083, Beijing, 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