Local descriptors for spatio-temporal recognition

  • Authors:

  • Ivan Laptev;Tony Lindeberg

  • Affiliations:

  • Computational Vision and Active Perception Laboratory (CVAP), Dept. of Numerical Analysis and Computing Science, KTH, Stockholm, Sweden;Computational Vision and Active Perception Laboratory (CVAP), Dept. of Numerical Analysis and Computing Science, KTH, Stockholm, Sweden

  • Venue:
  • SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
  • Year:
  • 2004

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Abstract

This paper presents and investigates a set of local space-time descriptors for representing and recognizing motion patterns in video. Following the idea of local features in the spatial domain, we use the notion of space-time interest points and represent video data in terms of local space-time events. To describe such events, we define several types of image descriptors over local spatio-temporal neighborhoods and evaluate these descriptors in the context of recognizing human activities. In particular, we compare motion representations in terms of spatio-temporal jets, position dependent histograms, position independent histograms, and principal component analysis computed for either spatio-temporal gradients or optic flow. An experimental evaluation on a video database with human actions shows that high classification performance can be achieved, and that there is a clear advantage of using local position dependent histograms, consistent with previously reported findings regarding spatial recognition.