Journal
APPLIED SCIENCES-BASEL
Volume 9, Issue 16, Pages -Publisher
MDPI
DOI: 10.3390/app9163337
Keywords
video surveillance system; anomaly detection; unsupervised learning; convolutional auto-encoder; variational auto-encoder
Categories
Funding
- National Natural Science Foundation of China [61701101, U1713216, U1613214]
- National Key R&D Program of China [2017YFC0821402]
- National Key Robot Project [2017YFB1300900, 2017YFB1301103]
- Fundamental Research Fund for the Central Universities of China [N172603001, N172604004, N172604002, N181602014]
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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.
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