Fetching the paper…
Reading the bibliography…
Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning.
Y. Zhao, B. Deng, C. Shen, Y. Liu, H. Lu, and X.-S. Hua, “Spatio-temporal autoencoder for video anomaly detection,” in Proceedings of the 2017 ACM on Multimedia Conference , ser. MM ’17. New York, NY, USA: ACM, 2017, pp. 1933–1941. [Online]. Available: http://doi.acm.org/10.1145/3123266.3123451
1941
Earlier work this paper cites.
V. Mahadevan, W.-X. LI, V. Bhalodia, and N. Vasconcelos, “Anomaly detection in crowded scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition , 2010, pp. 1975–1981
1981
Earlier work this paper cites.
D. Helbing and P. Molnar, “Social force model for pedestrian dynamics,” Physical review E , vol. 51, no. 5, p. 4282, 1995
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
M. E. Tipping and C. M. Bishop, “Mixtures of probabilistic principal component analyzers,” Neural computation , vol. 11, no. 2, pp. 443–482, 1999
1999
Earlier work this paper cites.
S. Hawkins, H. He, G. Williams, and R. Baxter, “Outlier detection using replicator neural networks,” in DaWaK , vol. 2454. Springer, 2002, pp. 170–180
2002
Earlier work this paper cites.
L. Wiskott and T. J. Sejnowski, “Slow feature analysis: Unsupervised learning of invariances,” Neural computation , vol. 14, no. 4, pp. 715–770, 2002
2002
Earlier work this paper cites.
T. Fawcett, “An introduction to roc analysis,” Pattern recognition letters , vol. 27, no. 8, pp. 861–874, 2006
2006
Earlier work this paper cites.
A. Basharat, A. Gritai, and M. Shah, “Learning object motion patterns for anomaly detection and improved object detection,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on . IEEE, 2008, pp. 1–8
2008
Earlier work this paper cites.
A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz, “Robust real-time unusual event detection using multiple fixed-location monitors,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 30, no. 3, pp. 555–560, 2008
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th international conference on Machine learning . ACM, 2008, pp. 1096–1103
2008
Earlier work this paper cites.
F. Creutzig and H. Sprekeler, “Predictive coding and the slowness principle: An information-theoretic approach,” Neural Computation , vol. 20, no. 4, pp. 1026–1041, 2008
2008
Earlier work this paper cites.
Y. Benezeth, P.-M. Jodoin, V. Saligrama, and C. Rosenberger, “Abnormal events detection based on spatio-temporal co-occurences,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on . IEEE, 2009, pp. 2458–2465
2009
Earlier work this paper cites.
J. Kim and K. Grauman, “Observe locally, infer globally: a space-time mrf for detecting abnormal activities with incremental updates,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on . IEEE, 2009, pp. 2921–2928
2009
Earlier work this paper cites.
A. Zaharescu and R. Wildes, “Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing,” in European Conference on Computer Vision . Springer, 2010, pp. 563–576
2010
Earlier work this paper cites.
D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, “Why does unsupervised pre-training help deep learning?” Journal of Machine Learning Research , vol. 11, no. Feb, pp. 625–660, 2010
2010
Earlier work this paper cites.
M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt, “Sequential deep learning for human action recognition,” in International Workshop on Human Behavior Understanding . Springer, 2011, pp. 29–39
2011
Earlier work this paper cites.
B. Zhao, L. Fei-Fei, and E. P. Xing, “Online detection of unusual events in videos via dynamic sparse coding,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition . IEEE Computer Society, 2011
2011
Earlier work this paper cites.
J. Masci, U. Meier, D. Cireşan, and J. Schmidhuber, “Stacked convolutional auto-encoders for hierarchical feature extraction,” Artificial Neural Networks and Machine Learning–ICANN 2011 , pp. 52–59, 2011
2011
Earlier work this paper cites.
A. Ng, “Sparse autoencoder,” CS294A Lecture notes , vol. 72, no. 2011, pp. 1–19, 2011
2011
Earlier work this paper cites.
S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio, “Contractive auto-encoders: Explicit invariance during feature extraction,” in Proceedings of the 28th international conference on machine learning (ICML-11) , 2011, pp. 833–840
2011
Earlier work this paper cites.
O. P. Popoola and K. Wang, “Video-based abnormal human behavior recognition : A review,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) , vol. 42, no. 6, pp. 865–878, 2012
2012
Earlier work this paper cites.
V. Saligrama and Z. Chen, “Video anomaly detection based on local statistical aggregates,” in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on . IEEE, 2012, pp. 2112–2119
2012
Earlier work this paper cites.
Z. Zhang and D. Tao, “Slow feature analysis for human action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 34, no. 3, pp. 436–450, 2012
2012
Earlier work this paper cites.
V. R. Kompella, M. Luciw, and J. Schmidhuber, “Incremental slow feature analysis: Adaptive low-complexity slow feature updating from high-dimensional input streams,” Neural Computation , vol. 24, no. 11, pp. 2994–3024, 2012
2012
Earlier work this paper cites.
C. Lu, J. Shi, and J. Jia, “Abnormal event detection at 150 fps in matlab,” 2013
2013
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
T. Wang and H. Snoussi, “Histograms of optical flow orientation for abnormal events detection,” in 2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) , 2013
2013
Earlier work this paper cites.
A. Wiesel, O. Bibi, and A. Globerson, “Time varying autoregressive moving average models for covariance estimation.” IEEE Trans. Signal Processing , vol. 61, no. 11, pp. 2791–2801, 2013
2013
Cited alongside, same era.
M. W. Diederik P Kingma, “Stochastic gradient vb and the variational auto-encoder,” in Proceedings of the 2nd International Conference on Learning Representations (ICLR) , 2014
2014
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Cited alongside, same era.
W. Li, V. Mahadevan, and N. Vasconcelos, “Anomaly detection and localization in crowded scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2014
2014
Cited alongside, same era.
C. Vondrick, H. Pirsiavash, and A. Torralba, “Generating videos with scene dynamics,” in Advances In Neural Information Processing Systems , 2016, pp. 613–621
2016
Later among the works it cites.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 961–971
2016
Later among the works it cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016, http://www.deeplearningbook.org
2016
Later among the works it cites.
M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis, “Learning temporal regularity in video sequences,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 733–742
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Shalev-Shwartz and S. Ben-David, Understanding machine learning: From theory to algorithms . Cambridge university press, 2014
2014
Cited alongside, same era.
V. Kantorov and I. Laptev, “Efficient feature extraction, encoding and classification for action recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 2593–2600
2014
Cited alongside, same era.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2014, pp. 1725–1732
2014
Cited alongside, same era.
G. Alain and Y. Bengio, “What regularized auto-encoders learn from the data-generating distribution,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 3563–3593, 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
L. Sun, K. Jia, T.-H. Chan, Y. Fang, G. Wang, and S. Yan, “Dl-sfa: deeply-learned slow feature analysis for action recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 2625–2632
2014
Cited alongside, same era.
S. Razakarivony and F. Jurie, “Discriminative autoencoders for small targets detection,” in Pattern Recognition (ICPR), 2014 22nd International Conference on . IEEE, 2014, pp. 3528–3533
2014
Cited alongside, same era.
2014
Cited alongside, same era.
M. Sabokrou, M. Fathy, and M. Hoseini, “Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder,” Electronics Letters , vol. 52, no. 13, pp. 1122–1124, 2016
2016
Later among the works it cites.
J. R. Medel, Anomaly Detection Using Predictive Convolutional Long Short-Term Memory Units . Rochester Institute of Technology, 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
X. Hu, S. Hu, Y. Huang, H. Zhang, and H. Wu, “Video anomaly detection using deep incremental slow feature analysis network,” IET Computer Vision , vol. 10, no. 4, pp. 258–265, 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
R. Leyva, V. Sanchez, and C.-T. Li, “The lv dataset: A realistic surveillance video dataset for abnormal event detection,” in Biometrics and Forensics (IWBF), 2017 5th International Workshop on . IEEE, 2017, pp. 1–6
2017
Later among the works it cites.
A. A. Abuolaim, W. K. Leow, J. Varadarajan, and N. Ahuja, “On the essence of unsupervised detection of anomalous motion in surveillance videos,” in International Conference on Computer Analysis of Images and Patterns . Springer, 2017, pp. 160–171
2017
Later among the works it cites.
A. Edison and J. C. V., “Optical acceleration for motion description in videos,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2017, pp. 1642–1650
2017
Later among the works it cites.
Y. S. Chong and Y. H. Tay, “Abnormal event detection in videos using spatiotemporal autoencoder,” in International Symposium on Neural Networks . Springer, 2017, pp. 189–196
2017
Later among the works it cites.
R. Chalapathy, A. K. Menon, and S. Chawla, “Robust, deep and inductive anomaly detection,” in ECML PKDD 2017 : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery , 2017
2017
Later among the works it cites.
D. Xu, Y. Yan, E. Ricci, and N. Sebe, “Detecting anomalous events in videos by learning deep representations of appearance and motion,” Computer Vision and Image Understanding , vol. 156, pp. 117 – 127, 2017, image and Video Understanding in Big Data
2017
Later among the works it cites.
H. Vu, T. D. Nguyen, A. Travers, S. Venkatesh, and D. Phung, Energy-Based Localized Anomaly Detection in Video Surveillance . Springer International Publishing, 2017, pp. 641–653
2017
Later among the works it cites.
M. Sabokrou, M. Fayyaz, M. Fathy, and R. Klette, “Deep-cascade: Cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenes,” IEEE Transactions on Image Processing , vol. 26, no. 4, pp. 1992–2004, 2017
2017
Later among the works it cites.
W. Luo, W. Liu, and S. Gao, “Remembering history with convolutional lstm for anomaly detection,” in Multimedia and Expo (ICME), 2017 IEEE International Conference on . IEEE, 2017, pp. 439–444
2017
Later among the works it cites.
A. Munawar, P. Vinayavekhin, and G. De Magistris, “Spatio-temporal anomaly detection for industrial robots through prediction in unsupervised feature space,” in Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on . IEEE, 2017, pp. 1017–1025
2017
Later among the works it cites.
D. D’Avino, D. Cozzolino, G. Poggi, and L. Verdoliva, “Autoencoder with recurrent neural networks for video forgery detection,” in IS&T International Symposium on Electronic Imaging: Media Watermarking, Security, and Forensics , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs, “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,” in International Conference on Information Processing in Medical Imaging . Springer, 2017, pp. 146–157
2017
Later among the works it cites.
2017
Later among the works it cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” 2017
2017
Later among the works it cites.
R. Zhang, P. Isola, and A. A. Efros, “Split-brain autoencoders: Unsupervised learning by cross-channel prediction,” in CVPR , 2017
2017
Later among the works it cites.
A. Dimokranitou, “Adversarial autoencoders for anomalous event detection in images,” Master’s thesis, 2017
2017
Later among the works it cites.
A. Munawar, P. Vinayavekhin, and G. De Magistris, “Limiting the reconstruction capability of generative neural network using negative learning,” in 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP, Roppongi, Tokyo, Japan, 2017 , 2017
2017
Later among the works it cites.