Recognizing Non-Stationary Walking based on Gait Analysis using Laser Scanners
In this paper the authors propose a method for recognizing non-stationary walking based on a gait analysis using multiple laser range scanners. The proposed method consists of the following procedures: (1) people tracking; (2) detection of gait features; (3) recognition of non-stationary walking. First, people tracking is performed by recognizing patterns in which the range data obtained near ankle rhythmically. Next, gait analysis is performed by the spatio-temporal clustering using Mean Shift algorithm. Finally, One Class Support Vector Machine (One Class SVM) is applied for learning and classifying a non-stationary walking. The experiment in a station concourse in Tokyo shows the overall accuracy of 98.4% by the proposed method.