Techniques for orientation independent gait analysis

Abstract

Gait recognition algorithms are being increasingly widely researched, however a common assumption is that the subject will be presented side on to the camera. In practice it may not be possible to capture data from this view, so a useful gait recognition algorithm will have to provide a measure of orientation independence. Three gait recognition algorithms are examined and found to perform poorly with nonnormal orientation. The complex detail used for recognition can not be translated between orientations in a holistic silhouette manner. It is shown that orientation independent features can be extracted using a human model. The algorithm is developed and tested on live captured data and found to perform better across orientations than silhouette based approaches. The performance recorded at a single orientation is lower than that of other approaches, however only the motion of the subject is currently used for recognition. More accurate motion estimation will increase performance as will the inclusion of other model based features.

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