Article Dans Une Revue International Journal of Distributed Sensor Networks Année : 2015

Abnormal event detection via multikernel learning for distributed camera networks

Résumé

Distributed camera networks play an important role in public security surveillance. Analyzing video sequences from cameras set at different angles will provide enhanced performance for detecting abnormal events. In this paper, an abnormal detection algorithm is proposed to identify unusual events captured by multiple cameras. The visual event is summarized and represented by the histogram of the optical flow orientation descriptor, and then a multikernel strategy that takes the multiview scenes into account is proposed to improve the detection accuracy. A nonlinear one-class SVM algorithm with the constructed kernel is then trained to detect abnormal frames of video sequences. We validate and evaluate the proposed method on the video surveillance dataset PETS.

Fichier principal
Vignette du fichier
15.cam.abnormal.pdf (1.65 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence
Loading...

Dates et versions

hal-01965567 , version 1 (26-12-2018)

Licence

Identifiants

Citer

Tian Wang, Jie Chen, Paul Honeine, Hichem Snoussi. Abnormal event detection via multikernel learning for distributed camera networks. International Journal of Distributed Sensor Networks, 2015, 2015 (Article ID 989450), pp.1-9. ⟨10.1155/2015/989450⟩. ⟨hal-01965567⟩
80 Consultations
151 Téléchargements

Altmetric

Partager

  • More