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)

Abstract

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)
Pages1303-1304
Number of pages2
ISBN (Print)978-1-4503-1089-5
DOIs
Publication statusPublished - Oct 2012
Event20th ACM international conference on Multimedia - Nara, Japan
Duration: 29 Oct 20122 Nov 2012

Conference

Conference20th ACM international conference on Multimedia
CountryJapan
CityNara
Period29/10/122/11/12

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