Earticle

초록

영어

Compressive tracking is considerably popular in the visual tracking community in recent years. The very strong theoretic support from compressive sensing motivates many researchers to follow and there are a wide range of compressive trackers with attractive performances. The goal of this paper is to overview some of the most recent state-of-the-art compressive trackers in the literature. First, a variety of compressive trackers are thoroughly introduced and summarized. Second, extensive analyses from different perspectives, including random measurement matrix, compressive features, feature selection strategy and so forth, aim to provide readers a good understanding of the strengths and weaknesses of different trackers. Finally, several possible future trends for compressive trackers are outlined to hopefully bring some insights to interesting researchers.

목차

Abstract
 1. Introduction
 2. Compressive Tracking Framework
 3. Compressive Trackers
  3.1. Random Measurement Matrix and Compressive Features
  3.2. Feature Selection Strategy
  3.3. Particle Filter Framework
 4. Future Trends
 Acknowledgment
 References

키워드

저자

• Zhongyi Hu [ Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, Zhejiang, China ]
• Lei Xiao [ School of Marine Science and Technology, Northwestern Polytechnic University, Xi’an 710072, Shanxi, China ]
• Fei Teng [ School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430070, Hubei, 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