Infrared and Laser Engineering, Volume. 51, Issue 6, 20210547(2022)
A video anomaly detection method based on deep autoencoding Gaussian mixture model
[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] [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] [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] [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] [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] [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] [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] [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] [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] [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] [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] [19] Springenberg J, Dosovitskiy A, Brox T, et al. Striving f simplicity: The all convolutional [C]International Conference on Learning Representations, 2015.
[20] [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] [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).
Get Citation
Copy Citation Text
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
Category: Photoelectric measurement
Received: Aug. 7, 2021
Accepted: --
Published Online: Dec. 20, 2022
The Author Email: Mo Haining (sunny_cj@21cn.com)