Extraction and classification of visual motion patterns for hand gesture recognition

Ming Hsuan Yang, Narendra Ahuja

Research output: Contribution to journalConference article

31 Citations (Scopus)


We present a new method for extracting and classifying motion patterns to recognize hand gestures. First, motion segmentation of the image sequence is generated based on a multiscale transform and attributed graph matching of regions across frames. This produces region correspondences and their affine transformations. Second, color information of motion regions is used to determine skin regions. Third, human head and palm regions are identified based on the shape and size of skin areas in motion. Finally, affine transformations defining a region's motion between successive frames are concatenated to construct the region's motion trajectory. Gestural motion trajectories are then classified by a time-delay neural network trained with backpropagation learning algorithm. Our experimental results show that hand gestures can be recognized well using motion patterns.

Original languageEnglish
Pages (from-to)892-897
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publication statusPublished - 1998 Dec 1
EventProceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Santa Barbara, CA, USA
Duration: 1998 Jun 231998 Jun 25


All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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