• Infrared and Laser Engineering
  • Vol. 51, Issue 6, 20210547 (2022)
Youkun Zhong1 and Haining Mo2、*
Author Affiliations
  • 1Physics and Mechanical & Electrical Engineering School, Hechi University, Yizhou 546300, China
  • 2HTC VIVEDU School of Technology, Guangxi University of Science and Technology, Liuzhou 545006, China
  • show less
    DOI: 10.3788/IRLA20210547 Cite this Article
    Youkun Zhong, Haining Mo. A video anomaly detection method based on deep autoencoding Gaussian mixture model[J]. Infrared and Laser Engineering, 2022, 51(6): 20210547 Copy Citation Text show less
    References

    [1] M Sabokrou, M Fayyaz, M Fathy, et al. Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes. Computer Vision and Image Understanding, 172, 88-97(2018).

    [2] C Li, Z Han, Q Ye, et al. Visual abnormal behavior detection based on trajectory sparse reconstruction analysis. Neurocomputing, 119, 94-100(2013).

    [3] F Jiang, J Yuan, S A Tsaftaris, et al. Anomalous video event detection using spatiotemporal context. Computer Vision and Image Understanding, 115, 323-333(2011).

    [4] Li W, Mahadevan V, Voncelos N. Anomaly detection localization in crowded scene [J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2014, 36(1): 1832.

    [5] Reddy V, Serson C, Lovell B. Improved anomaly detection in crowded scenes via cellbased analysis of feground speed, size texture [C]IEEE Computer Society Conference on Computer Vision Pattern Recognition Wkshops (CVPRW), 2011: 5561.

    [6] S Wang, E Zhu, J Yin, et al. Video anomaly detection and localization by local motion based joint video representation and OCELM. Neurocomputing, 277, 161-175(2018).

    [7] Kaur P, Gangadharappa M, Gautam S. An overview of anomaly detection in video surveillance [C]International Conference on Advances in Computing, Communication Control wking (ICACCCN), 2018.

    [8] J Schmidhuber. Deep learning in neural networks: An overview. Neural Networks, 61, 326-366(2015).

    [9] Y Lecun, Y Bengio, G Hinton. Deep learning. Nature, 521, 436-444(2015).

    [10] Hasan M, Choi J, Neumanny J, et al. Learning tempal regularity in video sequences [C]Proceedings of IEEE Conference on Computer Vision Pattern Recognition, 2016: 770778.

    [11] Gong D, Liu L, Le V, et al. Memizing nmality to detect anomaly: Memyaugmented deep autoencoder f unsupervised anomaly detection [C]IEEECVF Conference on Computer Vision Pattern Recognition, 2019: 18.

    [12] Ravanbakhsh M, Sangio E, Nabi M, et al. Abnmal event detection in videos using generative adversarial s [C]Proceedings of the IEEE International Conference on Image Processing (ICIP) 2017: 15.

    [13] Ravanbakhsh M, Sangio E, Nabi M, et al. Training adversarial discriminats f crosschannel abnmal event detection in crowds [C]Winter Conference on Applications of Computer Vision, 2019: 18961904.

    [14] MG Narasimhan, SK S. Dynamic video anomaly detection and localization using sparse denoising autoencoders. Multimedia Tools Appl, 77, 1317313195(2018).

    [15] Sabzalian B, Marvi H, Ahmadyfard A. Deep sparse features f anomaly detection localization in video [C]4th International Conference on Pattern Recognition Image Analysis (IPRIA), 2019: 173178

    [16] Lin S, Yang H, Tang X, et al. Social MIL: Interactionaware f crowd anomaly detection [C]16th IEEE International Conference on Advanced Video Signal Based Surveillance (AVSS), 2019: 18.

    [17] Y Fan, G Wen, D Li, et al. Video anomaly detection and localization via gaussianmixture fully convolutional variational autoencoder. Computer Vision and Image Understanding, 195, 102920(2020).

    [18] Liu W, Luo W, Lian D, et al. Future frame prediction f anomaly detectiona new baseline [C]IEEECVF Conference on Computer Vision Pattern Recognition, 2018: 65366545.

    [19] Springenberg J, Dosovitskiy A, Brox T, et al. Striving f simplicity: The all convolutional [C]International Conference on Learning Representations, 2015.

    [20] Luo W, Liu W, Gao S. Remembering histy with convolutional lstm f anomaly detection [C]IEEE International Conference on Multimedia Expo (ICME), 2017: 439444.

    [21] Luo W, Liu W, Gao S. A revisit of sparse coding based anomaly detection in stacked rnn framewk [C]IEEE International Conference on Computer Vision, 2017: 341349.

    [22] Dong Wang, Xiaojun Zhang, Lihua Dai. Video anomaly detection and localization via deep Gaussian process regression. Chinsese Journal of Scientific Instrument, 35, 158-164(2021).

    [23] Bo Yu, Fuqing Tian, Weige Liang. Fault diagnosis based on a deep convolution variational autoencoder network. Journal of Electronic Measurenment and Instrument, 39, 27-35(2018).

    Youkun Zhong, Haining Mo. A video anomaly detection method based on deep autoencoding Gaussian mixture model[J]. Infrared and Laser Engineering, 2022, 51(6): 20210547
    Download Citation