Earticle

초록

영어

Based on the problems that target appears rotation and noise interference in complex environment, an improved multi-feature adaptive fusion tracking method is proposed. The algorithm adopts unscented Kalman particle filter (UPF) to update the measurement information in the sample particles, better overcome the problem of the particle weight degradation. In addition, in order to overcome the defects of additive and multiplicative fusion algorithm in the feature selection, the multiple adaptive fusion characteristics method that target color distribution and scale invariance feature (SIFT) are used as complementary information. Experimental results show that the proposed method is superior to the traditional methods which are based on fixed weight or standard particle filter.

목차

Abstract
 1. Introduction
 2. Filtering Algorithm Principle
  2.1. Standard Particle Filter Algorithm
  2.2. Unscented Kalman Particle Filter
 3. The Implementation of the Adaptive Multi-feature Fusion
  3.1. Tracking Model
  3.2. Feature Fusion
  3.3. Algorithm Implementation
 4. Experiments and Results Analysis
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Yibo Li [ School of Automation, Shenyang Aerospace University, Liao Ning, China ]
• Xuezheng Zhuang [ School of Automation, Shenyang Aerospace University, Liao Ning, China ]
• Yanmei Liu [ School of Automation, Shenyang Aerospace University, Liao Ning, 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