In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality detection tasks.

Abnormal event detection in videos using generative adversarial nets / Ravanbakhsh, M.; Nabi, M.; Sangineto, E.; Marcenaro, L.; Regazzoni, C.; Sebe, N.. - 2017-:(2017), pp. 1577-1581. ((Intervento presentato al convegno 24th IEEE International Conference on Image Processing, ICIP 2017 tenutosi a China National Convention Center (CNCC), chn nel 2017 [10.1109/ICIP.2017.8296547].

Abnormal event detection in videos using generative adversarial nets

M. Nabi;E. Sangineto;N. Sebe
2017

Abstract

In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality detection tasks.
Proceedings - International Conference on Image Processing, ICIP
Ravanbakhsh, Mahdyar
New York
IEEE Computer Society
9781509021758
Ravanbakhsh, M.; Nabi, M.; Sangineto, E.; Marcenaro, L.; Regazzoni, C.; Sebe, N.
Abnormal event detection in videos using generative adversarial nets / Ravanbakhsh, M.; Nabi, M.; Sangineto, E.; Marcenaro, L.; Regazzoni, C.; Sebe, N.. - 2017-:(2017), pp. 1577-1581. ((Intervento presentato al convegno 24th IEEE International Conference on Image Processing, ICIP 2017 tenutosi a China National Convention Center (CNCC), chn nel 2017 [10.1109/ICIP.2017.8296547].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/215289
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