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Proceedings Paper

Abnormal events detection method for surveillance video using an improved autoencoder with multi-modal input
Author(s): Haichun Yue; Shigang Wang; Jian Wei; Yan Zhao
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Paper Abstract

This paper introduces an algorithm to solve the the anomaly behavior detection problem of surveillance video through an improved autoencoder with multimodal inputs. Using 3D convolution and 3D deconvolution, and the decoder adds a feature map corresponding to the encoder on a specific layer to enhance the image detail information. Taking the RGB frame and the optical flow as inputs, abnormality scores are calculated according to the reconstruction error for locating the abnormal segment. Experiments conducted in the CUHK Avenue dataset, the UCSD Pedestrian dataset and the Behave dataset, our approach works best compare to the original approach. While improving the AUC, due to the use of unsupervised learning, a lot of labeling time is saved, which is more in line with the diversity and contingency of abnormal behavior in real life.

Paper Details

Date Published: 18 November 2019
PDF: 9 pages
Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111870U (18 November 2019); doi: 10.1117/12.2538160
Show Author Affiliations
Haichun Yue, Jilin Univ. (China)
Shigang Wang, Jilin Univ. (China)
Jian Wei, Jilin Univ. (China)
Yan Zhao, Jilin Univ. (China)

Published in SPIE Proceedings Vol. 11187:
Optoelectronic Imaging and Multimedia Technology VI
Qionghai Dai; Tsutomu Shimura; Zhenrong Zheng, Editor(s)

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