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An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders

1
College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
2
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3337; https://doi.org/10.3390/app9163337
Received: 25 June 2019 / Revised: 27 July 2019 / Accepted: 12 August 2019 / Published: 14 August 2019
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Abstract

Anomaly detection in crowded scenes is an important and challenging part of the intelligent video surveillance system. As the deep neural networks make success in feature representation, the features extracted by a deep neural network represent the appearance and motion patterns in different scenes more specifically, comparing with the hand-crafted features typically used in the traditional anomaly detection approaches. In this paper, we propose a new baseline framework of anomaly detection for complex surveillance scenes based on a variational auto-encoder with convolution kernels to learn feature representations. Firstly, the raw frames series are provided as input to our variational auto-encoder without any preprocessing to learn the appearance and motion features of the receptive fields. Then, multiple Gaussian models are used to predict the anomaly scores of the corresponding receptive fields. Our proposed two-stage anomaly detection system is evaluated on the video surveillance dataset for a large scene, UCSD pedestrian datasets, and yields competitive performance compared with state-of-the-art methods. View Full-Text
Keywords: video surveillance system; anomaly detection; unsupervised learning; convolutional auto-encoder; variational auto-encoder video surveillance system; anomaly detection; unsupervised learning; convolutional auto-encoder; variational auto-encoder
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Xu, M.; Yu, X.; Chen, D.; Wu, C.; Jiang, Y. An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders. Appl. Sci. 2019, 9, 3337.

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