Extraction of 2D motion trajectories and its application to hand gesture recognition

Ming Hsuan Yang, Narendra Ahuja, Mark Tabb

Research output: Contribution to journalArticlepeer-review

Abstract

We present an algorithm for extracting and classifying two-dimensional motion in an image sequence based on motion trajectories. First. a multiscale segmentation is performed to generate homogeneous regions in each frame. Regions between consecutive frames are then matched to obtain two-view correspondences. Affine transformations are computed from each pair of corresponding regions to define pixel matches. Pixels matches over consecutive image pairs are concatenated to obtain pixel-level motion trajectories across the image sequence, Motion patterns are learned from the extracted trajectories using a time-delay neural network. We apply the proposed method to recognize 40 hand gestures of American Sign Language. Experimental results show that motion patterns of hand gestures can be extracted and recognized accurately using motion trajectories.

Original languageEnglish (US)
Pages (from-to)1061-1074
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Volume24
Issue number8
DOIs
StatePublished - Aug 2002

Keywords

  • American Sign Language
  • Hand gesture recognition
  • Motion analysis
  • Motion segmentation
  • Motion trajectory
  • Time-delay neural network

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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