Earticle

초록

영어

Tracking with a discriminative classifier becomes popular recently. The online updating makes it easy to adapt to target appearance variations. However, this also brings drifting problem. It’s necessary to find a tracking method with strong adaptivity and anti-drifting ability. In this paper, an online semi-supervised boosting method is proposed at first, and based on it, we propose a novel tracking framework that treats samples differently when updating the classifier under different conditions. This tracking framework can significantly alleviate the drifting problem and keep adaptive enough to appearance variations. Experimental results on challenging videos show that our method can track accurately and robustly， and outperform many other state-of-the-art trackers.

목차

Abstract
 1. Introduction
 2. Online Semi-supervised Boosting
  2.1. Loss Function for Labeled and Unlabeled Samples
  2.2. Learning based on Gradient Descent Principle
  2.3. Online semi-supervised Boosting Algorithm
 3. Visual Tracking based on Semi-supervised Boosting
  3.1. Prior Model and Classifier Initialization
  3.2. Process of Target Tracking
 4. Experimental Results and Discussion
  4.1. Parameter Sensitivity
  4.2. Anti-drifting Ability
  4.3. Adaptivity
  4.4. Comprehensive Performance Compariation with other Trackers
 5. Conclusions
 Acknowledgement
 References

키워드

저자

• Wenhui Dong [ School of Control Science and Engineering, Shandong University, Department of Physics, Dezhou University ]
• Faliang Chang [ School of Control Science and Engineering, Shandong University ]
• Zijian Zhao [ School of Control Science and Engineering, Shandong University ]

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