首页|A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos
A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos
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NSTL
Elsevier
Anomaly detection plays an important role in surveillance video since it maintains public safety efficiently with low cost. In current works, anomaly detection methods based on reconstruction with deep learning has been extensively studied for the powerful representation capacity. These methods use convolutional neural networks to learn model for describing normality at training and detect anomalies according to reconstruction error at testing. However, excessive representation capacity of neural networks will also bring disadvantages to anomaly detection when it is powerful enough to reconstruct abnormal infor-mation. For this reason, we proposed two solutions; firstly, a cascade model which conducts pixel recon-struction followed by optical flow prediction is designed. The conversion from frame to optical flow learns the correlation between object appearance and motion, while pixel reconstruction enlarges the optical flow prediction error to conduct effective anomaly detection. Secondly, the generalization ability evalua-tion based on pseudo-anomaly is proposed, which is used to evaluate the ability of model to represent anomaly, thus selecting an optimal model for anomaly detection. The selected model achieves AUC 88.9% on Avenue, 82.6% on Ped1, 97.7% on Ped2, and 70.7% on ShanghaiTech datasets. Extensive ablation ex-periments have verified the effectiveness of our method. Code will be released at https://github.com/Xia-Chen/Cascade_Reconstruction. (c) 2021 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
