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Advances in Multimedia
Volume 2018, Article ID 2087574, 8 pages
Research Article

Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention

1Department of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu, China
2Changzhou Vocational Institute Textile and Garment, Changzhou, Jiangsu, China

Correspondence should be addressed to Ling Zou; nc.ude.uzcc@gniluoz

Received 22 April 2018; Accepted 15 August 2018; Published 3 September 2018

Academic Editor: Yong Luo

Copyright © 2018 Biao Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose an anomaly detection approach by learning a generative model using deep neural network. A weighted convolutional autoencoder- (AE-) long short-term memory (LSTM) network is proposed to reconstruct raw data and perform anomaly detection based on reconstruction errors to resolve the existing challenges of anomaly detection in complicated definitions and background influence. Convolutional AEs and LSTMs are used to encode spatial and temporal variations of input frames, respectively. A weighted Euclidean loss is proposed to enable the network to concentrate on moving foregrounds, thus restraining background influence. Moving foregrounds are segmented from the input frames using robust principal component analysis decomposition. Comparisons with state-of-the-art approaches indicate the superiority of our approach in anomaly detection. Generalization of anomaly detection is improved by enforcing the network to focus on moving foregrounds.