Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition

  • Authors:
  • Ming Hsuan Yang;Narendra Ahuja;Mark Tabb

  • Affiliations:
  • Honda Fundemental Research Labs, 800 CaliforniaSt., Mountain Vew, CA;Department of Computer Science and Beckman Institute, University Illinois at Urbana-champaign, Urbana, Il;Vexel Corporation, 4909 Nautilus Court, Boulder, CO

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2002

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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.