Show simple item record Chathuramali, KGM Rodrigo, R 2018-11-07T21:32:25Z 2018-11-07T21:32:25Z
dc.description.abstract Action recognition in a video plays an important role in computer vision and finds many applications in areas such as surveillance, sports, and elderly monitoring. Existing methods mostly rely on stationary backgrounds. Action recognition in dynamic backgrounds typically requires standard preprocessing steps such as motion compensation, background modeling, moving object detection and object recognition. The errors of the motion compensation step and background modelling increase the mis-detections. Therefore action recognition in dynamic background is challenging. In this paper, we use a combination of pose characterized by a silhouette and optic flows synthesized into a histogram. This enables us to classify the movement of the actor versus movement of the background. We use four background models to extract the silhouette from the frame. We use SVM to recognize actions, according to several evaluation protocols. We perform several experiments and compare over a diverse set of challenging videos, including the new Change Detection Challenge Dataset. Our results perform better than existing methods. en_US
dc.language.iso en en_US
dc.subject Dynamic backgrounds, background modeling, AMM, FDM, GMM, JBFM, SVM en_US
dc.title Action recognition using a spatio-temporal model in dynamic scenes en_US
dc.type Conference-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Electronic and Telecommunication Engineering en_US
dc.identifier.year 2014 en_US
dc.identifier.conference 7th International Conference on Information and Automation for Sustainability en_US en_US en_US

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