Online human gesture recognition from motion data streams

Xin Zhao, Xue Li, Chaoyi Pang, Xiaofeng Zhu, Quan Z. Sheng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

64 Citations (Scopus)


Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: I) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the stat-of-the-art approaches.

Original languageEnglish
Title of host publicationMM 2013 - Proceedings of the 2013 ACM Multimedia Conference
Place of PublicationNew York, NY
PublisherACM Press
Number of pages10
ISBN (Print)9781450324045
Publication statusPublished - 2013
Externally publishedYes
Event21st ACM International Conference on Multimedia, MM 2013 - Barcelona, Spain
Duration: 21 Oct 201325 Oct 2013


Other21st ACM International Conference on Multimedia, MM 2013


  • depth camera
  • feature extraction
  • gesture recognition


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