Abstract
AbstractIn this paper, we present a “key segment” mining approach for human action recognition. Our model is able to locate discriminative segments for action samples via multiple instance learning. Moreover, we propose a dynamic pooling approach to automatically find the optimal length of segment for each action sample. In addition, an effective feature is proposed for action recognition with 3D skeleton joints. It can effectively capture informative motion and shape cues of skeletons, and leads to a compact and discriminative representation. The experimental results validate the effectiveness of the proposed human action recognition method on two benchmark datasets (i.e., MSR Action3D and UCF-Kinect). Moreover, our method demonstrates superior accuracy than previous methods of using only skeleton data on MSR Action3D, and achieves the state-of-the-art performance on UCF-Kinect.Keywordsaction recognitionmultiple instance learningskeleton featuredynamic pooling
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.
