Computer Science ›› 2023, Vol. 50 ›› Issue (5): 146-154.doi: 10.11896/jsjkx.220400227

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Pseudo-abnormal Sample Selection for Video Anomaly Detection

ZHAO Song, FU Hao, WANG Hongxing   

  1. Key Laboratory of Dependable Service Computing in Cyber Physical Society(Chongqing University), Ministry of Education, Chongqing 400044, China
    School of Big Data & Software Engineering,Chongqing University,Chongqing 400044,China
  • Received:2022-04-22 Revised:2022-09-12 Online:2023-05-15 Published:2023-05-06
  • About author:ZHAO Song,born in 1997,postgra-duate,is a student member of China Computer Federation.His main research interests include computer vision and video anomaly detection.
    WANG Hongxing,born in 1985,Ph.D,associate professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include computer vision and pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(61976029) and Key Project of Chongqing Technology Innovation and Application Development(cstc2021jscx-gksbX0033).

Abstract: Unsupervised video anomaly detection methods generally use normal video data to train an anomaly detection model through frame reconstruction or frame prediction.However,normal videos usually contain a large number of background frames and similar scenes,which are quite redundant,leading to inefficient modeling for video anomaly detection.To address this issue,this paper proposes a pseudo-abnormal sample selection method,which iteratively selects video frames with high abnormal scores from original videos to build a new concise training pool for video anomaly detection based on future frame prediction.As for the detection model,this paper designs a two-path U-Net architecture,where each path has a different sampling frequency on video frames so that spatial-temporal features of videos can be better extracted and utilized from multiple scales.In the two-path U-Net,each layer shares a memory module to strengthen the impact of typical training data for future frame prediction and video anomaly detection.Experimental evaluation on benchmark video datasets demonstrates the efficiency and effectiveness of the proposed method.

Key words: Video surveillance, Anomaly detection, Sample selection, Memory model, Spatial-Temporal feature

CLC Number: 

  • TP391.4
[1]CHANDOLA V,BANERJEEA,KUMAR V.Anomaly detec-tion:A survey[J].ACM Computing Surveys,2009,41(3):1-58.
[2]CHALAPATHY R,CHAWLA S.Deep learning for anomalydetection:A survey[J].arXiv:1901.03407,2019.
[3]FU Z,HU W,TAN T.Similarity based vehicle trajectory clustering and anomaly detection[C]//Proceedings of the IEEE International Conference on Image Processing.2005:II-602.
[4]HASANM,CHOI J,NEUMANN J,et al.Davis.Learning temporal regularity in video sequences[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:733-742.
[5]ABATI D,PORRELLO A,CALDERARA S,et al.Latent space autoregression for novelty detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:481-490.
[6]LIU W,LUO W,LIAN D,et al.Future frame prediction for anomaly detection-a new baseline[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6536-6545.
[7]HAO H W,JIANG R R.Training sample selection method for neural networks based on nearest neighbor rule[J].Acta Automatica Sinica,2007,33(12):1247-1251.
[8]MARKOVITZ A,SHARIR G,FRIEDMAN I,et al.Graph embedded pose clustering for anomaly detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2020:10536-10544.
[9]FEICHTENHOFER C,FAN H,MALIK J,et al.Slowfast networks for video recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:6202-6211.
[10]LENG J X,TAN M P,HU B,et al.Video Anomaly Detection Based on Implicit View Transformation[J].Computer Science,2022,49(2):142-148.
[11]PARK H,NOH J,HAM B.Learning memory guided normality for anomaly detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2020:14372-14381.
[12]RONNEBERGER O,FISCHER P,BROX T.U-Net:convolu-tional networks for biomedical image segmen-tation[C]//Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention.2015:234-241.
[13]FANG Z,LIANG J,ZHOU J,et al.Anomaly Detection with Bidirectional Consistency in Videos[J].IEEE Transactions on Neural Networks and Learning Systems,2022,33(3):1079-1092.
[14]XU D,YAN Y,RICCI E,et al.Detecting anomalous events invideos by learning deep representations of appearance and motion[J].Computer Vision and Image Understanding,2017,156:117-127.
[15]YU G,WANG D,CAI X,et al.Cloze test helps:Effective video anomaly detection via learning to complete video events[C]//Proceedings of the ACM International Conference on Multimedia.2020:583-591.
[16]CHANG Y,TU Z,XIE W,et al.Clustering driven deep autoencoder for video anomaly detection[C]//Proceedings of the European Conference on Computer Vision.2020:329-345.
[17]HAO Y,LI J,WANG N,et al.Spatiotemporal consistency-enhanced network for video anomaly detection[J].Pattern Recognition,2022,121:108232.
[18]GEORGESCU M,BARBALAU A,IONESCU R,et al.Anomaly detection in video via self-supervised and multi-task learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:12742-12752.
[19]GRAVES A,MOHAMED A,HINTON G.Speech recognition with deep recurrent neural networks[C]//Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing.2013:6645-6649.
[20]SHI X,CHEN Z,WANG H,et al.Convolutional LSTM Net-work:A machine learning approach for precipitation nowcasting[C]//Advances in Neural Information Processing Systems.2015:802-810.
[21]WESTON J,CHOPRA S,BORDES A.Memory networks[J].arXiv:1410.3916,2014.
[22]WANG X,DU Y,LIN S,et al.adVAE:a self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection[J].Knowledge-Based Systems,2019,190:105187.
[23]HAN T,XIE W A.Zisserman.Memory augmented dense predictive coding for video representation learning[C]//Proceedings of the European Conference on Computer Vision.2020:312-329.
[24]SUKHBAATAR S,SZLAM A,WESTON J,et al.End-to-end memory networks[J].arXiv:1503.08895,2015.
[25]MILLER A,FISCH A,DODGE J,et al.Key-value memory networks for directly reading documents[J].arXiv:1606.03126,2016.
[26]GONG D,LIU L,LE V,et al.Memorizing normality to detect anomaly:Memory-augmented deep autoencoder for unsupervised anomaly detection[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:1705-1714.
[27]ZHOU Y,REN Q C,NIU H B.Research on training sample data selection methods[J].Computer Science,2020,47(11A):402-408.
[28]PLUTOWSKI M,WHITE H.Selecting concise training setsfrom clean data[J].IEEE Transactions on Neural Networks,1993,4(2):305-318
[29]WANG J,NESKOVIC P,COOPER L N.Training data selection for support vector machines[C]//International Conference on Natural Computation,2005:554-564.
[30]CORTES C,VAPNIK V.Support-vector networks[J].Machine learning,1995,20(3):273-297.
[31]TANG K,LIN M,MINKU F,et al.Selective negative correla-tion learning approach to incremental learning[J].Neurocomputing,2009,72(13/14/15):2796-2805.
[32]CONG Y,YUAN J,LIU J.Sparse reconstruction cost for abnormal event detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2011:3449-3456.
[33]MAHADEVAN V,LI W,BHALODIA V,et al.Anomaly detection in crowded scenes[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2010:1975-1981.
[34]LU C,SHI J,AND JIA J.Abnormal event detection at 150 fps in matlab[C]//Proceedings of the IEEE International Conference on Computer Vision.2013:2720-2727.
[35]WANG Z,ZOU Y,ZHANG Z.Cluster Attention Contrast for Video Anomaly Detection[C]//Proceedings of the International Conference on Multimedia.2020:2463-2471.
[36]LI B,LEROUX S,SIMOENS P.Decoupled appearance and motion learning for efficient anomaly detection in surveillance video[J].Computer Vision and Image Understanding,2021,210:103249.
[37]ZHONG Y,CHEN X,JIANG J,et al.A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos[J].Pattern Recognition,2022,122:108336.
[38]CHANG Y,TU Z,XIE W,et al.Video anomaly detection with spatio-temporal dissociation[J].Pattern Recognition,2022,122:108213.
[1] ZHANG Guohua, YAN Xuefeng, GUAN Donghai. Anomaly Detection of Time-series Based on Multi-modal Feature Fusion [J]. Computer Science, 2023, 50(6A): 220700094-7.
[2] WU Hanxiao, ZHAO Qianqian, ZHU Jianqing, ZENG Huanqiang, DU Jixiang, LIAO Yun. Metric Regularized Infrared and Visible Cross-modal Person Re-identification [J]. Computer Science, 2023, 50(6A): 221100046-8.
[3] SUN Kaiwei, WANG Zhihao, LIU Hu, RAN Xue. Maximum Overlap Single Target Tracking Algorithm Based on Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220400023-5.
[4] SUN Xuekui, DAI Hua, ZHOU Jianguo, YANG Geng, CHEN Yanli. LTTFAD:Log Template Topic Feature-based Anomaly Detection [J]. Computer Science, 2023, 50(6): 313-321.
[5] ZHANG Renbin, ZUO Yicong, ZHOU Zelin, WANG Long, CUI Yuhang. Multimodal Generative Adversarial Networks Based Multivariate Time Series Anomaly Detection [J]. Computer Science, 2023, 50(5): 355-362.
[6] CUI Jingsong, ZHANG Tongtong, GUO Chi, GUO Wenfei. Network Equipment Anomaly Detection Based on Time Delay Feature [J]. Computer Science, 2023, 50(3): 371-379.
[7] RAO Dan, SHI Hongwei. Study on Air Traffic Flow Recognition and Anomaly Detection Based on Deep Clustering [J]. Computer Science, 2023, 50(3): 121-128.
[8] XU Tian-hui, GUO Qiang, ZHANG Cai-ming. Time Series Data Anomaly Detection Based on Total Variation Ratio Separation Distance [J]. Computer Science, 2022, 49(9): 101-110.
[9] WANG Xin-tong, WANG Xuan, SUN Zhi-xin. Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network [J]. Computer Science, 2022, 49(8): 314-322.
[10] DU Hang-yuan, LI Duo, WANG Wen-jian. Method for Abnormal Users Detection Oriented to E-commerce Network [J]. Computer Science, 2022, 49(7): 170-178.
[11] GAO Zhi-yu, WANG Tian-jing, WANG Yue, SHEN Hang, BAI Guang-wei. Traffic Prediction Method for 5G Network Based on Generative Adversarial Network [J]. Computer Science, 2022, 49(4): 321-328.
[12] SHEN Shao-peng, MA Hong-jiang, ZHANG Zhi-heng, ZHOU Xiang-bing, ZHU Chun-man, WEN Zuo-cheng. Three-way Drift Detection for State Transition Pattern on Multivariate Time Series [J]. Computer Science, 2022, 49(4): 144-151.
[13] WU Yu-kun, LI Wei, NI Min-ya, XU Zhi-cheng. Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder [J]. Computer Science, 2022, 49(3): 144-151.
[14] LENG Jia-xu, TAN Ming-pi, HU Bo, GAO Xin-bo. Video Anomaly Detection Based on Implicit View Transformation [J]. Computer Science, 2022, 49(2): 142-148.
[15] WANG Bo, HUA Qing-yi, SHU Xin-feng. Study on Anomaly Detection and Real-time Reliability Evaluation of Complex Component System Based on Log of Cloud Platform [J]. Computer Science, 2022, 49(12): 125-135.
Full text



No Suggested Reading articles found!