|Table of Contents|

Vehicle Appearance Model Tracker Integrating Incremental Principal Component Analysis and Particle Filter(PDF)

《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年01期
Page:
33-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Vehicle Appearance Model Tracker Integrating Incremental Principal Component Analysis and Particle Filter
Author(s):
Wu Gang123Zeng Xiaoqin3Su Shoubao1Wang Chishe12
(1.School of Computer Engineering,JinLing Institute of Technology,Nanjing 211169,China)(2.Institute of Big Data,JinLing Institute of Technology,Nanjing 211169,China)(3.College of Computer and Information,Hohai University,Nanjing 210098,China)
Keywords:
vehicle trackingappearance modelautocorrelation matrixincremental learningparticle filter
PACS:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2017.01.006
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.

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