원문정보
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
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