Enhanced Convolutional Neural Network for Abnormal Event Detection in Video Streams

Abstract : Abnormal event detection is a complex task in computer vision. It is one among the most difficult challenges in crowded scene analysis, given the huge variety of events to analyze and the frequent occlusions that can occur. Ideally, to meet this challenge, it would be necessary to have a large number of normal and abnormal training examples. However, the high variability between events and their complexity make it almost impossible to provide enough abnormal training samples. To overcome these constraints, the solution may reside in a robust one-class learning algorithm. In this paper, we propose a novel one-class neural network technique suitable for outliers detection. This network takes advantage of the deep learning ability in image processing, while meeting the constraint of using only training samples from the target class. Our network is composed of a Convolutional Auto Encoder (CAE) with two loss functions. The first one is a standard mean squared error (MSE) and the second is a proposed compactness loss function particularly adapted for one class learning. This network allows us to not only extract robust spatiotemporal features but also ensures that all the target class representations are sufficiently compact to be effectively isolated from outliers. The proposed algorithm is evaluated on the challenging USCD Ped2 dataset for abnormal video event detection and localization. Both qualitative and quantitative experiments show competitive performances compared to the state-of-the-art methods.
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Conference papers
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https://hal-utt.archives-ouvertes.fr/hal-02308862
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Tuesday, October 8, 2019 - 5:41:52 PM
Last modification on : Wednesday, October 9, 2019 - 1:37:46 AM

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Samir Bouindour, Ronghua Hu, Hichem Snoussi. Enhanced Convolutional Neural Network for Abnormal Event Detection in Video Streams. 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Jun 2019, Sardinia, Italy. pp.172-178, ⟨10.1109/AIKE.2019.00039⟩. ⟨hal-02308862⟩

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