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Modeling the Temporal Extent of Actions

  • Scott Satkin
  • Martial Hebert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

In this paper, we present a framework for estimating what portions of videos are most discriminative for the task of action recognition. We explore the impact of the temporal cropping of training videos on the overall accuracy of an action recognition system, and we formalize what makes a set of croppings optimal. In addition, we present an algorithm to determine the best set of croppings for a dataset, and experimentally show that our approach increases the accuracy of various state-of-the-art action recognition techniques.

Keywords

Action Recognition Temporal Extent Training Video Multiple Instance Learning Action Recognition System 
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 2010

Authors and Affiliations

  • Scott Satkin
    • 1
  • Martial Hebert
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon University 

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