An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet
Permanent link to this recordhttp://hdl.handle.net/10754/631506
MetadataShow full item record
AbstractWe address in this paper the problem of abnormal event detection in video-surveillance. In this context, we use only normal events as training samples. We propose to use a modified version of pretrained 3D residual convolutional network to extract spatio-temporal features, and we develop a robust classifier based on the selection of vectors of interest. It is able to learn the normal behavior model and detect potentially dangerous abnormal events. This unsupervised method prevents the marginalization of normal events that occur rarely during the training phase since it minimizes redundancy information, and adapt to the appearance of new normal events that occur during the testing phase. Experimental results on challenging datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level in anomaly detection task.
CitationBouindour S, Snoussi H, Hittawe M, Tazi N, Wang T (2019) An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet. Applied Sciences 9: 757. Available: http://dx.doi.org/10.3390/app9040757.
SponsorsFunding: This work is supported by the French regional council of Grand-Est and the European regional development fund-FEDER. Acknowledgments: The authors are grateful to anonymous reviewers for their comments that considerably enhanced the quality of the paper.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).