Estimating the pedestrian 3D motion indoor via hybrid tracking model

被引：0
|
作者：
Yu X.-S. [1 ]
Zhao W. [1 ]
Liu P. [1 ]
Tang X.-L. [1 ]
机构：
[1] School of Computer Science and Technology, Harbin Institute of Technology
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2010年 / 36卷 / 06期
关键词：
Hybrid tracking model; Limbs motion correlation coefficient; M-estimator; Pedestrian 3D motion; Self-occlusion state detection model;
D O I：
10.3724/SP.J.1004.2010.00773
中图分类号：
学科分类号：
摘要：
Focusing on self-occlusion of pedestrian 3D motion estimation, the paper proposed a hybrid tracking model particle filter algorithm. First, using the self-occlusion state detecting model, the pedestrian motion can be detected and divided into four self-occlusion states. Second, via hybrid tracking model, different tracking patterns are proposed to track pedestrian motion on different self-occlusion states. And finally, for estimating the pedestrian motion on the self-occlusion state, we proposed the on-line training algorithm based on M-estimate to train the limbs motion correlation coefficient. The result of experiment showed that our algorithm acquires good results of estimating pedestrian 3D motion on the occlusion state and advances the accuracy of estimating pedestrian 3D motion. Copyright © 2010 Acta Automatica Sinica. All right reserved.
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收藏
页码：773 / 784
页数：11
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