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Sensors 2016, 16(2), 161; doi:10.3390/s16020161

An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor

School of Software, Xidian University, Xi’an 710071, China
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Author to whom correspondence should be addressed.
Academic Editor: Yajing Shen
Received: 19 November 2015 / Revised: 22 January 2016 / Accepted: 22 January 2016 / Published: 28 January 2016
(This article belongs to the Special Issue Sensors for Robots)
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Abstract

Continuous human action recognition (CHAR) is more practical in human-robot interactions. In this paper, an online CHAR algorithm is proposed based on skeletal data extracted from RGB-D images captured by Kinect sensors. Each human action is modeled by a sequence of key poses and atomic motions in a particular order. In order to extract key poses and atomic motions, feature sequences are divided into pose feature segments and motion feature segments, by use of the online segmentation method based on potential differences of features. Likelihood probabilities that each feature segment can be labeled as the extracted key poses or atomic motions, are computed in the online model matching process. An online classification method with variable-length maximal entropy Markov model (MEMM) is performed based on the likelihood probabilities, for recognizing continuous human actions. The variable-length MEMM method ensures the effectiveness and efficiency of the proposed CHAR method. Compared with the published CHAR methods, the proposed algorithm does not need to detect the start and end points of each human action in advance. The experimental results on public datasets show that the proposed algorithm is effective and highly-efficient for recognizing continuous human actions. View Full-Text
Keywords: continuous human action recognition; online segmentation; maximum entropy Markov model; Kinect continuous human action recognition; online segmentation; maximum entropy Markov model; Kinect
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Zhu, G.; Zhang, L.; Shen, P.; Song, J. An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor. Sensors 2016, 16, 161.

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