Local Derivative Pattern for Action Recognition in Depth Images

Abstract : 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.
Type de document :
Article dans une revue
Multimedia Tools and Applications, Springer Verlag, 2017, pp.1-19. 〈10.1007/s11042-017-4749-z〉
Liste complète des métadonnées

Littérature citée [44 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01657473
Contributeur : François Charpillet <>
Soumis le : jeudi 7 décembre 2017 - 08:34:13
Dernière modification le : samedi 9 décembre 2017 - 01:11:51

Fichier

manuscript.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Xuan Son Nguyen, Thanh Phuong Nguyen, François Charpillet, Ngoc-Son Vu. Local Derivative Pattern for Action Recognition in Depth Images. Multimedia Tools and Applications, Springer Verlag, 2017, pp.1-19. 〈10.1007/s11042-017-4749-z〉. 〈hal-01657473〉

Partager

Métriques

Consultations de la notice

24

Téléchargements de fichiers

11