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Abnormal event detection based on deep autoencoder fusing optical flow

Abstract : As an important research topic in computer vision, abnormal detection has gained more and more attention. In order to detect abnormal events effectively, we propose a novel method using optical flow and deep autoencoder. In our model, optical flow of the original video sequence is calculated and visualized as optical flow image, which is then fed into a deep autoencoder. Then the deep autoencoder extract features from the training samples which are compressed to low dimension vectors. Finally, the normal and abnormal samples gather separately in the coordinate axis. In the evaluation, we show that our approach outperforms the existing methods in different scenes, in terms of accuracy.
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https://hal-utt.archives-ouvertes.fr/hal-03320609
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Submitted on : Monday, August 16, 2021 - 11:02:37 AM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM

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Meina Qiao, Tian Wang, Jiakun Li, Ce Li, Zhiwei Lin, et al.. Abnormal event detection based on deep autoencoder fusing optical flow. 2017 36th Chinese Control Conference (CCC), Jul 2017, Dalian, China. pp.11098-11103, ⟨10.23919/ChiCC.2017.8029129⟩. ⟨hal-03320609⟩

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