南京师大学报（自然科学版）2017，Vol.40Issue(1)：33-38,6.DOI:10.3969/j.issn.1001-4616.2017.01.006
融合增量主成分分析与粒子滤波的车辆表观模型跟踪
Vehicle Appearance Model Tracker Integrating Incremental Principal Component Analysis and Particle Filter
摘要
Abstract
Aiming at the difficulties on stably and timely tracking vehicle on the scenes such as volatile moving direction,varying pose and distance,illumination change,etc.,integrating autocorrelation matrix,incremental learning on IPCA and particle filter algorithm,new kind of vehicle tracking methods using appearance model is proposed.When beginning at original tracking time,the proposed method can timely learn the characteristic subspace images of vehicle,using autocorrelation matrix and eigen value decomposition.Based on IPCA incremental learning,likelihood probability density is computed on subspace mean and eigenvector,increasing computational precision on weights of particles on particle filter algorithm.The tracking results demonstrate that success tracking rate of the proposed AM-IPCA vehicle tracking method is raised to 95.1％ ～ 96.4％,compared with 82.7％ ～ 92.3％ of P.Hall-IPCA and 92.1％ ～ 95.2％ of D.Ross-IPCA appearance model tracking method.关键词
车辆跟踪/表观模型/自相关矩阵/增量学习/粒子滤波Key words
vehicle tracking/appearance model/autocorrelation matrix/incremental learning/particle filter分类
信息技术与安全科学引用本文复制引用
吴刚,曾晓勤,苏守宝,王池社..融合增量主成分分析与粒子滤波的车辆表观模型跟踪[J].南京师大学报（自然科学版）,2017,40(1):33-38,6.基金项目
金陵科技学院高层次人才科研启动项目(jit-rcyj-201508)、国家自然科学基金项目(61375121)、国家自然科学基金项目(61305011)、南京市经信委项目(交通大数据公共服务平台)、南京市科委重大项目(大数据驱动下的大型客运枢纽监控预警与应急处置). （jit-rcyj-201508）
