Sparse Shift-Invariant Representation of Local 2D Patterns and Sequence Learning for Human Action Recognition - Archive ouverte HAL Access content directly
Conference Papers Year : 2012

Sparse Shift-Invariant Representation of Local 2D Patterns and Sequence Learning for Human Action Recognition

Abstract

Most existing methods for action recognition mainly rely on manually engineered features which, despite their good performances, are highly problem dependent. We propose in this paper a fully automated model, which learns to classify human actions without using any prior knowledge. A convolutional sparse auto- encoder learns to extract sparse shift-invariant representations of the 2D local patterns present in each video frame. The evolution of these mid-level features is learned by a Recurrent Neural Network trained to classify each sequence. Experimental results on the KTH dataset show that the proposed approach outperforms existing models which rely on learned-features, and gives comparable results with the best related works.
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Dates and versions

hal-01353014 , version 1 (10-08-2016)

Identifiers

  • HAL Id : hal-01353014 , version 1

Cite

Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, Atilla Baskurt. Sparse Shift-Invariant Representation of Local 2D Patterns and Sequence Learning for Human Action Recognition. 21st International Conference on Pattern Recognition (ICPR), Nov 2012, Tsukuba Science City, Japan. pp.3823-3826. ⟨hal-01353014⟩
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