Contextual Statistics of Space-Time Ordered Features for Human Action Recognition

Piotr Bilinski 1 Francois Bremond 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The bag-of-words approach with local spatio-temporal features have become a popular video representation for action recognition. Recent methods have typically focused on capturing global and local statistics of features. However, existing approaches ignore relations between the features, particularly space-time arrangement of features, and thus may not be discriminative enough. Therefore, we propose a novel figure-centric representation which captures both local density of features and statistics of space-time ordered features. Using two benchmark datasets for human action recognition, we demonstrate that our representation enhances the discriminative power of features and improves action recognition performance, achieving 96.16% recognition rate on popular KTH action dataset and 93.33% on challenging ADL dataset.
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Communication dans un congrès
9th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Sep 2012, Beijing, China. 2012
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Dernière modification le : mardi 24 juillet 2018 - 15:48:03
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Piotr Bilinski, Francois Bremond. Contextual Statistics of Space-Time Ordered Features for Human Action Recognition. 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Sep 2012, Beijing, China. 2012. 〈hal-00718293〉

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