Spatio-Temporal Unity Networking for Video Anomaly Detection

Anomaly detection in video surveillance is challenging due to the variety of anomaly types and definitions, which limit the use of supervised techniques. As such, auto-encoder structures, a type of classical unsupervised method, have recently been utilized in this field. These structures consist of...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.172425-172432
Main Authors: Li, Yuanyuan, Cai, Yiheng, Liu, Jiaqi, Lang, Shinan, Zhang, Xinfeng
Format: Article
Language:English
Subjects:
Anomalies
Anomaly detection
Clips
Coders
ConvLSTM
Convolution
Datasets
Decoding
Error detection
Feature extraction
Frames (data processing)
Optical flow
Optical losses
Spatial data
Training
U-Net
video
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Summary:Anomaly detection in video surveillance is challenging due to the variety of anomaly types and definitions, which limit the use of supervised techniques. As such, auto-encoder structures, a type of classical unsupervised method, have recently been utilized in this field. These structures consist of an encoder followed by a decoder and are typically adopted to restructure a current input frame or predict a future frame. However, regardless of whether a 2D or 3D autoencoder structure is adopted, only single-scale information from the previous layer is typically used in the decoding process. This can result in a loss of detail that could potentially be used to predict or reconstruct video frames. As such, this study proposes a novel spatio-temporal U-Net for frame prediction using normal events and abnormality detection using prediction error. This framework combines the benefits of U-Nets in representing spatial information with the capabilities of ConvLSTM for modeling temporal motion data. In addition, we propose a new regular score function, consisting of a prediction error for not only the current frame but also future frames, to further improve the accuracy of anomaly detection. Extensive experiments on common anomaly datasets, including UCSD (98 video clips in total) and CUHK Avenue (30 video clips in total), validated the performance of the proposed technique and we achieved 96.5% AUC for the Ped2 dataset, which is much better than existing autoencoder-based and U-Net-based methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2954540