Conference Proceedings

Deep Learning and One-class SVM based Anomalous Crowd Detection

M Yang, S Rajasegarar, SM Erfani, C Leckie

2019 International Joint Conference on Neural Networks (IJCNN) | IEEE | Published : 2019

Abstract

Anomalous event detection in videos is an important and challenging task. This paper proposes a deep representation approach to the problem, which extracts and represents features in an unsupervised way. This algorithm can detect anomalous activity like standing statically and loitering among a crowd of people. Our proposed framework is a two-channel scheme by using feature channels extracted from the appearance and foreground of the original video. Two hybrid deep learning architectures SDAE-DBN-PSVM (a four-layer Stacked Denoising Auto-encoder with three-layer Deep Belief Nets and Plane-based one class SVM) are implemented for these two channels to learn the high-level feature representati..

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