关键词: 前车追踪, 粒子滤波, SULBP特征, 自适应降维, 自适应融合策略

Abstract: The feature fusion based particle filter algorithm can make tracking system more robust, by fusing different features. However, currently these algorithms have some drawbacks, such as insignificant feature differences, poor in real-time process, and confined fusion tactics. Based on that, an improved fusion algorithm for preceding vehicles tracking system is proposed, which adopts new intensified edge information SULBP feature, and enhances the real-time feature extraction through adaptive dimensionality reduction method. In addition, this fusion tactic appears more universal by designing the adaptive fusion algorithm based on the distribution state of the particle set. Indicated by the experiments result, this multi-feature fusion particle filter  algorithm is improved in both tracking performance and feasibility.

Key words: preceding vehicles tracking, particle filter, SULBP feature, adaptive dimensionality reduction, adaptive fusion method