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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
