Abstract
Spatio-temporal interest points (STIPs) are local invariant features of a video and are both distinctive and descriptive and therefore can be applied in action recognition; however, most existing STIP detectors extend spatial descriptions by adding a temporal component for the appearance description, which separate spatio-temporal domain correlations in the spatio-temporal domain and only implicitly capture motion information. Therefore, by regarding the video as a 3-d structure, this research aims to develop a novel STIP detector which synthetically exploits appearance and motion information using Clifford algebra. This study firstly establishes a general Clifford algebra model for video and then builds the unified model of appearance and motion (UMAM) based thereon to synthetically analyse appearance and motion information. Subsequently, in the spirit of the well-known Harris 3-d detector, a UMAM-based spatio-temporal Harris corner detector (UMAM-Harris) for videos is developed. The experimental results indicate that the UMAM-Harris detector proposed in this study extracts the UMAM-Harris corners that contain distinctive features in the spatial domain and reflect substantial motion in the time domain, and it offers a better performance than the traditional STIP detection algorithms used in video action recognition.
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.
