Minimal-latency human action recognition using reliable-inference

  • Authors:
  • James W. Davis;Ambrish Tyagi

  • Affiliations:
  • Department of Computer Science and Engineering, Ohio State University, 491 Dreese Lab, 2015 Neil Avenue, Columbus, OH 43210, USA;Department of Computer Science and Engineering, Ohio State University, 491 Dreese Lab, 2015 Neil Avenue, Columbus, OH 43210, USA

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2006

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Abstract

We present a probabilistic reliable-inference framework to address the issue of rapid detection of human actions with low error rates. The approach determines the shortest video exposures needed for low-latency recognition by sequentially evaluating a series of posterior ratios for different action classes. If a subsequence is deemed unreliable or confusing, additional video frames are incorporated until a reliable classification to a particular action can be made. Results are presented for multiple action classes and subsequence durations, and are compared to alternative probabilistic approaches. The framework provides a means to accurately classify human actions using the least amount of temporal information.