计算机工程与应用2017，Vol.53Issue(19)：173-178,230,7.DOI:10.3778/j.issn.1002-8331.1701-0104
用于前车追踪的多特征融合粒子滤波算法改进
Improvement on multi-feature fusion particle filter algorithm for preceding vehicles tracking
摘要
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.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Key words
preceding vehicles tracking/particle filter/SULBP feature/adaptive dimensionality reduction/adaptive fusion method分类
信息技术与安全科学引用本文复制引用
徐喆,胡亮..用于前车追踪的多特征融合粒子滤波算法改进[J].计算机工程与应用,2017,53(19):173-178,230,7.基金项目
国家自然科学基金(No.61374143). （No.61374143）
