Computer Science ›› 2022, Vol. 49 ›› Issue (8): 172-177.doi: 10.11896/jsjkx.210600061

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection

SUN Qi, JI Gen-lin, ZHANG Jie   

  1. School of Computer and Electronic Information/School of Artificial Intelligence,Nanjing Normal University,Nanjing,210023,China
  • Received:2021-06-04 Revised:2021-09-11 Published:2022-08-02
  • About author:SUN Qi,born in 1995,master.Her main research interests include big data ana-lysis and mining technology.
    JI Gen-lin,born in 1964,Ph.D,professor.His main research interests include big data analysis and mining techno-logy.
  • Supported by:
    National Natural Science Foundation of China(41971343).

Abstract: As the uncertainty of abnormal events,the method of future frame prediction is chosen to detect abnormal events in video.The prediction model is trained with normal samples,so that the model can accurately predict the future frames without abnormal events.However,it cannot predict video frames with unknown events.Combining with apparent constraints and motion constraints,generative adversarial network is used to train the generator model for prediction.In order to reduce the loss of relative target features,a nonlocal attention Unet generator (NA-UnetG) model is proposed to improve the prediction accuracy of generator and the accuracy of abnormal video event detection.Experiments on datasets CUHK Avenue and UCSD Ped2 validate the effectiveness of the proposed method.The results show that the AUC of the proposed method is better than that of other methods,reaches 83.4% and 96.3%,respectively.

Key words: Deep learning, Generative adversarial network, Non-local attention mechanism, Video anomaly event detection, Video prediction

CLC Number: 

  • TP391
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