Unsupervised Motion Classification by Means of Efficient Feature Selection and Tracking

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.

• Type: Conference
• Year: 2004
• Where: 3DPVT
• Authors: Angel Domingo Sappa, Niki Aifanti, Sotiris Malassiotis, Michael G. Strintzis
• Keywords: 3DPVT 2004, Human Motion Recognition, Motion Recognition, Stage Detects Peaks, Visualization