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Communication Dans Un Congrès Année : 2014

Representing Visual Appearance by Video Brownian Covariance Descriptor for Human Action Recognition

Résumé

This paper addresses a problem of recognizing human actions in video sequences. Recent studies have shown that methods which use bag-of-features and space-time features achieve high recognition accuracy. Such methods extract both appearance-based and motion-based features. This paper focuses only on appearance features. We proposeto model relationships between different pixel-level appearance features such as intensity and gradient using Brownian covariance, which is a natural extension of classical covariance measure. While classical covariance can model only linear relationships, Brownian covariance models all kinds of possible relationships. We propose a method to compute Brownian covariance on space-time volume of a video sequence. We show that proposed Video Brownian Covariance (VBC) descriptor carries complementary information to the Histogram of Oriented Gradients (HOG) descriptor. The fusion of these two descriptors gives a significant improvement in performance on three challenging action recognition datasets.
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Dates et versions

hal-01054943 , version 1 (11-08-2014)
hal-01054943 , version 2 (06-11-2014)

Identifiants

  • HAL Id : hal-01054943 , version 2

Citer

Piotr Bilinski, Michal Koperski, Slawomir Bak, François Bremond. Representing Visual Appearance by Video Brownian Covariance Descriptor for Human Action Recognition. AVSS - 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, IEEE, Aug 2014, Seoul, South Korea. ⟨hal-01054943v2⟩

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