Abstract
Recently, many gait recognition algorithms are proposed, and the optimal camera arrangement is necessary to maximize the performance. In this paper, we propose the optimal camera arrangement by using a performance model that considers observation conditions comprehensively. We select silhouette resolution, observation view, and its local and global changes as the observation conditions affecting the performance. Then, training sets composed of pairs of the observation conditions and the performance is obtained by gait recognition experiments under several camera arrangements. A performance model is constructed by applying Gaussian Processes Regression to the training set. The optimal arrangement is determined by estimating the performance for each camera arrangement with the performance model. The effectiveness of the proposed method is demonstrated by experiments of performance estimation with a training set including 17 subjects and the optimal camera arrangement.
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.
