Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks

This paper presents an anomaly detection method that is based on a sparse coding inspired Deep Neural Networks (DNN). Specifically, in light of the success of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC), where a temporally-coherent term is used to pres...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 43; no. 3; pp. 1070 - 1084
Main Authors:	Luo, Weixin, Liu, Wen, Lian, Dongze, Tang, Jinhui, Duan, Lixin, Peng, Xi, Gao, Shenghua
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Anomalies Anomaly detection Artificial neural networks Coding Computational efficiency Computing costs Datasets Deep learning Dictionaries Encoding Feature extraction Frames (data processing) Iterative methods Neural networks Optimization Recurrent neural networks Similarity Sparse coding stacked recurrent neural networks Training
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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