NDLI: Action and Gait Recognition From Recovered 3-D Human Joints

Content Provider	IEEE Xplore Digital Library
Author	Junxia Gu Xiaoqing Ding Shengjin Wang Youshou Wu
Copyright Year	1996
Abstract	A common viewpoint-free framework that fuses pose recovery and classification for action and gait recognition is presented in this paper. First, a markerless pose recovery method is adopted to automatically capture the 3-D human joint and pose parameter sequences from volume data. Second, multiple configuration features (combination of joints) and movement features (position, orientation, and height of the body) are extracted from the recovered 3-D human joint and pose parameter sequences. A hidden Markov model (HMM) and an exemplar-based HMM are then used to model the movement features and configuration features, respectively. Finally, actions are classified by a hierarchical classifier that fuses the movement features and the configuration features, and persons are recognized from their gait sequences with the configuration features. The effectiveness of the proposed approach is demonstrated with experiments on the Institut National de Recherche en Informatique et Automatique Xmas Motion Acquisition Sequences data set.
Page Count	13
File Size	1625369
Starting Page	1021
Ending Page	1033
File Format	PDF
ISSN	10834419
Volume Number	40
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Hidden Markov models Joints Fuses Motion analysis Legged locomotion Data mining Biological system modeling Video surveillance Security three-dimensional (3-D) human joints Action recognition exemplar-based hidden Markov model (HMM) gait recognition
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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