One shot learning gesture recognition with Kinect sensor

Di Wu, Fan Zhu, Ling Shao, Hui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Gestures are both natural and intuitive for Human-Computer-Interaction (HCI) and the one-shot learning scenario is one of the real world situations in terms of gesture recognition problems. In this demo, we present a hand gesture recognition system using the Kinect sensor, which addresses the problem of one-shot learning gesture recognition with a user-defined training and testing system. Such a system can behave like a remote control where the user can allocate a specific function using a prefered gesture by performing it only once. To adopt the gesture recognition framework, the system first automatically segments an action sequence into atomic tokens, and then adopts the Extended-Motion-History-Image (Extended-MHI) for motion feature representation. We evaluate the performance of our system quantitatively in Chalearn Gesture Challenge, and apply it to a virtual one shot learning gesture recognition system.
Original languageEnglish
Title of host publicationProceedings of the 20th ACM international conference on Multimedia
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Print)978-1-4503-1089-5
Publication statusPublished - Oct 2012
Event20th ACM international conference on Multimedia - Nara, Japan
Duration: 29 Oct 20122 Nov 2012


Conference20th ACM international conference on Multimedia

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