Abstract
Human gait (manner of walking) and activity analysis from video sources is currently attracting attention in the computer vision community. The best current gait recognition system has a 90% identification rate under reasonable conditions, while recognition rates significantly decrease with a change of clothing, shoes, surface, or pose. In this paper, we present a framework for semantic gait interpretation and recognition using a knowledgebased method. The proposed approach consists of three phases: i) knowledge acquisition, ii) learning, and iii) categorization. We can solve the current gait recognition problem by linking the ontology of a meaningful human concept and locomotion to numerical data.
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