Unsupervised Discovery, Modeling, and Analysis of Long Term Activities

  • Guido Pusiol
  • Francois Bremond
  • Monique Thonnat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)

Abstract

This work proposes a complete framework for human activity discovery, modeling, and recognition using videos. The framework uses trajectory information as input and goes up to video interpretation. The work reduces the gap between low-level vision information and semantic interpretation, by building an intermediate layer composed of Primitive Events. The proposed representation for primitive events aims at capturing meaningful motions (actions) over the scene with the advantage of being learned in an unsupervised manner. We propose the use of Primitive Events as descriptors to discover, model, and recognize activities automatically. The activity discovery is performed using only real tracking data. Semantics are added to the discovered activities (e.g., “Preparing Meal”, “Eating”) and the recognition of activities is performed with new datasets.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guido Pusiol
    • 1
  • Francois Bremond
    • 1
  • Monique Thonnat
    • 1
  1. 1.Pulsar, Inria - Sophia AntipolisFrance

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