Local Derivative Pattern for Action Recognition in Depth Images
Résumé
This paper proposes a new local descriptor for action recognition in depth images using second-order directional Local Derivative Patterns (LDPs). LDP relies on local deriva- tive direction variations to capture local patterns contained in an image region. Our proposed local descriptor combines different directional LDPs computed from three depth maps obtained by representing depth sequences in three orthogonal views and is able to jointly encode the shape and motion cues. Moreover, we suggest the use of Sparse Coding-based Fisher Vector (SCFVC) for encoding local descriptors into a global representation of depth sequences. SCFVC has been proven effective for object recognition but has not gained much attention for action recognition. We perform action recognition using Extreme Learn- ing Machine (ELM). Experimental results on three public benchmark datasets show the effectiveness of the proposed approach.
| Origine | Fichiers produits par l'(les) auteur(s) |
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