Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/129207
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Type: Conference paper
Title: Self-trained deep ordinal regression for end-to-end video anomaly detection
Author: Pang, G.
Yan, C.
Shen, C.
van den Hengel, A.
Bai, X.
Citation: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020 / pp.12170-12179
Publisher: IEEE
Publisher Place: online
Issue Date: 2020
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781728171692
ISSN: 1063-6919
2575-7075
Conference Name: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (14 Jun 2020 - 19 Jun 2020 : Virtual online)
Statement of
Responsibility: 
Guansong Pang, Cheng Yan, Chunhua Shen, Anton van den Hengel, Xiao Bai
Abstract: Video anomaly detection is of critical practical importance to a variety of real applications because it allows human attention to be focused on events that are likely to be of interest, in spite of an otherwise overwhelming volume of video. We show that applying self-trained deep ordinal regression to video anomaly detection overcomes two key limitations of existing methods, namely, 1) being highly dependent on manually labeled normal training data; and 2) sub-optimal feature learning. By formulating a surrogate two-class ordinal regression task we devise an end-toend trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data. Experiments on eight real-world video scenes show that our proposed method outperforms state-of-the-art methods that require no labeled training data by a substantial margin, and enables easy and accurate localization of the identified anomalies. Furthermore, we demonstrate that our method offers effective human-in-the-loop anomaly detection which can be critical in applications where anomalies are rare and the false-negative cost is high.
Rights: ©2020 IEEE
RMID: 1000028087
DOI: 10.1109/CVPR42600.2020.01219
Published version: https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding
Appears in Collections:Australian Institute for Machine Learning publications

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