Abnormal event detection in videos using generative adversarial nets

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 trai...

Full description

Saved in:
Bibliographic Details
Published in2017 IEEE International Conference on Image Processing (ICIP) pp. 1577 - 1581
Main Authors Ravanbakhsh, Mahdyar, Nabi, Moin, Sangineto, Enver, Marcenaro, Lucio, Regazzoni, Carlo, Sebe, Nicu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
abnormal event detection
crowd behaviour analysis
Gallium nitride
Generative Adversarial Networks
Generators
Image reconstruction
Optical imaging
Task analysis
Training
Video analysis
Videos
Online AccessGet full text
ISSN2381-8549
DOI10.1109/ICIP.2017.8296547

Cover

More Information
Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8296547