Advances in View-Invariant Human Motion Analysis: A Review

被引：165
作者：
Ji, Xiaofei [1 ,2 ,3 ]
Liu, Honghai [1 ]
机构：
[1] Univ Portsmouth, Inst Ind Res, Portsmouth PO1 3QL, Hants, England
[2] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 210016, Peoples R China
[3] Shenyang Inst Aeronaut Engn, Shenyang 110136, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2010年 / 40卷 / 01期
基金：
英国工程与自然科学研究理事会;
关键词：
Behavior understanding; human motion analysis; pose representation and estimation; view invariant; RECOGNITION; POSE; SURVEILLANCE; TRACKING; MODELS;
D O I：
10.1109/TSMCC.2009.2027608
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
As viewpoint issue is becoming a bottleneck for human motion analysis and its application, in recent years, researchers have been devoted to view-invariant human motion analysis and have achieved inspiring progress. The challenge here is to find a methodology that can recognize human motion patterns to reach increasingly sophisticated levels of human behavior description. This paper provides a comprehensive survey of this significant research with the emphasis on view-invariant representation, and recognition of poses and actions. In order to help readers understand the integrated process of visual analysis of human motion, this paper presents recent development in three major issues involved in a general human motion analysis system, namely, human detection, view-invariant pose representation and estimation, and behavior understanding. Public available standard datasets are recommended. The concluding discussion assesses the progress so far, and outlines some research challenges and future directions, and solution to what is essential to achieve the goals of human motion analysis.
引用
收藏
页码：13 / 24
页数：12
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