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Abnormal Event Detection Using Convolutional Neural Networks and 1-Class SVM classifier

Abstract : In this paper, we present a method based on deep learning for detection and localization of spatial and temporal abnormal events in surveillance videos using training samples containing only normal events. This work is divided into two stages, the first one is feature extraction for each patch of the input image using the first two convolution layers extracted from a pretrained CNN. In second stage, one-class SVM is trained with resultant features. The SVM classifier allows a fast and robust abnormal detection with respect to the presence of outliers in the training dataset. Experimental tests have conducted on UCSD Ped2 dataset, this dataset is considered as complex due to low resolution and presence of many occlusions. Our results showed high performance and were compared with state-of-the art methods.
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https://hal-utt.archives-ouvertes.fr/hal-03320613
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 11:05:27 AM
Last modification on : Friday, August 27, 2021 - 3:14:07 PM

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Samir Bouindour, Mohamad Mazen Hittawe, Sandy Mahfouz, Hichem Snoussi. Abnormal Event Detection Using Convolutional Neural Networks and 1-Class SVM classifier. 8th International Conference on Imaging for Crime Detection and Prevention (ICDP 2017), Dec 2017, Madrid, Spain. pp.1-6, ⟨10.1049/ic.2017.0040⟩. ⟨hal-03320613⟩

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