A Graphical Model for Human Activity Recognition

  • Rocío Díaz de León
  • Luis Enrique Sucar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

We propose a general model for visual recognition of human activities, based on a probabilistic graphical framework. The motion of each limb and the coordination between them is considered in a layered network that can represent and recognize a wide range of human activities. By using this model and a sliding window, we can recognize simultaneous activities in a continuous way. We explore two inference methods for obtaining the most probable set of activities per window: probability propagation and abduction. In contrast with the standard approach that uses several models, we use a single classifier for multiple activity recognition. We evaluated the model with real image sequences of 6 different activities performed continuously by different people. The experiments show high recall and recognition rates.

Keywords

Bayesian Network Video Sequence American Sign Human Activity Recognition Displacement Direction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Rocío Díaz de León
    • 1
  • Luis Enrique Sucar
    • 2
  1. 1.IPICyTSan Luis PotosíMéxico
  2. 2.ITESM CuernavacaCuernavacaMéxico

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