Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention

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ID: 7768
2018
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Abstract
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.
Reference Key
biao2018anomalyadvances Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yang, Biao;Cao, Jinmeng;Ni, Rongrong;Zou, Ling;Yang, Biao;Cao, Jinmeng;Ni, Rongrong;Zou, Ling;
Journal advances in multimedia
Year 2018
DOI
10.1155/2018/2087574
URL
https://www.hindawi.com/journals/am/2018/2087574/abs/
Keywords Keywords not found

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