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

Piotr Bilinski 1 Michal Koperski 1 Slawomir Bak 1 François Bremond 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : 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.
Keywords : computer visiosn action recognition
Type de document :
Communication dans un congrès
AVSS - 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Aug 2014, Seoul, South Korea. 2014
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Contributeur : Michal Koperski <>
Soumis le : jeudi 6 novembre 2014 - 15:00:51
Dernière modification le : mardi 24 juillet 2018 - 15:48:06
Document(s) archivé(s) le : vendredi 14 avril 2017 - 15:02:39

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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, Aug 2014, Seoul, South Korea. 2014. 〈hal-01054943v2〉

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