Human motion detection using Markov random fields

被引：6

作者：

Cao, Xiao-Qin ^{[1
]}

Liu, Zhi-Qiang ^{[1
]}

机构：

[1] City Univ Hong Kong, Sch Creat Media, Kowloon Tong, Hong Kong, Peoples R China

来源：

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING | 2010年 / 1卷 / 03期

关键词：

Human motion; Markov random fields; Relaxation labeling; HUMAN POSE; TRACKING; MODEL;

D O I：

10.1007/s12652-010-0015-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose Markov random fields (MRFs) to automatically detect a moving human body through minimizing the joint energy of the MRF for the velocity and relative position of body parts. The relaxation labeling algorithm is employed to find the best body part labeling configuration between MRFs and observed data. We detect a walking motion viewed monocularly based on point features, where some points are from the unoccluded body parts and some belong to the background. The results show that MRFs can detect human motions robustly and accurately.

引用

页码：211 / 220

页数：10

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