Abnormal event detection via multikernel learning for distributed camera networks

Tian Wang (1) , Jie Chen (2) , Paul Honeine (3) , Hichem Snoussi (3)

1 BUAA - Beihang University
2 University of Michigan [Ann Arbor]
3 LM2S - Laboratoire Modélisation et Sûreté des Systèmes

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.

Mots clés: machine learning; wireless sensor networks

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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⟩