A combined SVM/HCRF model for activity recognition based on STIPs trajectories - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

A combined SVM/HCRF model for activity recognition based on STIPs trajectories

Résumé

In this paper, we propose a novel human activity recognition approach based on STIPs' trajectories as local descriptors of video sequences. This representation compares favorably with state of art feature extraction methods. In addition, we investigate the use of SVM/HCRF combination for temporal sequence modeling, where SVM is applied locally on short video segments to produce probability scores, the latter being considered as the input vectors to HCRF. This method constitutes a new contribution to the state of the art on activity recognition task. The obtained results demonstrate that our method is efficient and compares favorably with state of the art methods on human activity recognition

Dates et versions

hal-01275239 , version 1 (17-02-2016)

Identifiants

Citer

Mouna Selmi, Mounim El Yacoubi, Bernadette Dorizzi. A combined SVM/HCRF model for activity recognition based on STIPs trajectories. ICPRAM 2013 : 2nd International Conference on Pattern Recognition Applications and Methods, Feb 2013, Barcelone, Spain. pp.568 - 572, ⟨10.5220/0004267405680572⟩. ⟨hal-01275239⟩
81 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More