自动化学报2011，Vol.37Issue(5)：550-559,10.DOI:10.3724/SP.J.1004.2011.00550
基于不确定性度量的多特征融合跟踪
Fusing Multiple Features for Object Tracking Based on Uncertainty Measurement
摘要
Abstract
This paper presents a novel tracking algorithm that fuses multiple features based on feature uncertainty measurement.It is based on the fact that tracking failure of particle filter often happens in the cases of low discriminative abilities of the observed features and disperse distributions of the sampled particles.To handle this failure, we first define a new feature uncertainty measurement method to adaptively adjust the relative contributions of different features.Then we introduce a serf-adaptive feature fusion strategy to overcome the shortcomings of product and sum fusion ones.This strategy effectively sharpens the distribution of the fused posterior, and makes the tracking leas sensitive to noises.Thereby, the tracking robustness is improved.An extensive number of comparative experiments show that the proposed algorithm is more stable and robust than the single feature, multiplicative fusion, and additive fusion tracking algorithms.关键词
目标跟踪/不确定性度量/粒子滤波/多特征融合Key words
Object tracking/ uncertainty measurement/ particle filter/ multiple features fusion引用本文复制引用
顾鑫,王海涛,汪凌峰,王颖,陈如冰,潘春洪..基于不确定性度量的多特征融合跟踪[J].自动化学报,2011,37(5):550-559,10.基金项目
国家自然科学基金(60873161,61005036,61005013),江苏省自然基金项目(BK2010503),苏州市科技局项目(SYG201024)资助 （60873161,61005036,61005013）
