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Second International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT'04)
Unsupervised Motion Classification by Means of Efficient Feature Selection and Tracking
Thessaloniki, Greece
September 06-September 09
ISBN: 0-7695-2223-8
Angel D. Sappa, Campus UAB, Barcelona, Spain
Niki Aifanti, Informatics & Telematics Institute, Greece
Sotiris Malassiotis, Informatics & Telematics Institute, Greece
Michael G. Strintzis, Informatics & Telematics Institute, Greece
This paper presents an efficient technique for human motion recognition; in particular, it is focused on labeling a movement as a walking or running displacement, which are the most frequent type of locomotion. The proposed technique consists of two stages and is based on the study of feature points' trajectories. The first stage detects peaks and valleys of points' trajectories, which are used on the second stage to discern whether the movement corresponds to a walking or a running displacement. Prior knowledge of human body kinematics structure together with the corresponding motion model are the basis for the motion recognition. Experimental results with different video sequences are presented.
Citation:
Angel D. Sappa, Niki Aifanti, Sotiris Malassiotis, Michael G. Strintzis, "Unsupervised Motion Classification by Means of Efficient Feature Selection and Tracking," 3dpvt, pp.912-917, Second International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT'04), 2004
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