NDLI: Solving boundary problem of the motion database for improved human motion recognition

Content Provider	IEEE Xplore Digital Library
Author	Eftakhar, S.M.A. Joo Kooi Tan Hyongseop Kim Ishikawa, S.
Copyright Year	2010
Description	Author affiliation: Department of Control Engineering, Kyushu Institute of Technology, Sensui-cho 1-1, Tobata, Kitakyushu, Fukuoka 804-8550, JAPAN (Eftakhar, S.M.A.; Joo Kooi Tan; Hyongseop Kim; Ishikawa, S.)
Abstract	Development of a high accuracy and robust human motion recognition system is the fundamental requirement to apply in the real-life applications. Therefore, the recognition systems are being investigated to improve the system's performance and to enhance with more functionality. As a result of such an investigation, the formerly adopted structured motion database, capable of suitable retrieval of known motion patterns, is modified for obtaining more improved performance over the earlier systems [11]. The earlier systems has the boundary problems for the motion points those lie on the edge of the searching boundary within the query space at the time of retrieving similar motions from within the motion database. This problem might lead to the misrecognition of human motions in high degree. To overcome this problem, we make use of two sets of query spaces, namely original and shifted query space set which is to be switched based on the input motion. Motion History Image (MHI) and Exclusive-OR (OR) image are used as the motion representations, and the directional eigenspace strategy is used for constructing vector spaces. The results guarantee the significant improvement of the proposed system.
Starting Page	698
Ending Page	701
File Size	857952
Page Count	4
File Format	PDF
ISBN	9781424462773
e-ISBN	9781424462803
DOI	10.1109/ICELCE.2010.5700789
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-18
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Hidden Markov models Feature extraction Computational modeling Pixel Database systems Motion History Image Boundary problem structured motion database human motion recognition B-Tree
Content Type	Text
Resource Type	Article

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