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The detection of abnormal behaviours in crowded scenes has to deal with many challenges.
V. Mahadevan, W. Li, V. Bhalodia, N. Vasconcelos, Anomaly detection in crowded scenes, in: Computer Vision Pattern Recognition, 2010, pp. 1975–1981
1981
Earlier work this paper cites.
M. Sabokrou, M. Fayyaz, M. Fathy, R. Klette, Deep-cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes, IEEE Trans. Image Processing (2017) 1992–2004
2004
Earlier work this paper cites.
D. Zhang, D. Gatica-Perez, S. Bengio, I. McCowan, Semi-supervised adapted HMMS for unusual event detection, in: Computer Vision Pattern Recognition, Vol. 1, 2005, pp. 611–618
2005
Earlier work this paper cites.
C. Piciarelli, G. L. Foresti, On-line trajectory clustering for anomalous events detection, Pattern Recognition Letters volume 27 (2006) 1835–1842
2006
Earlier work this paper cites.
W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan, S. Maybank, A system for learning statistical motion patterns, IEEE Trans. Pattern Analysis Machine Intelligence 28 (9) (2006) 1450–1464
2006
Earlier work this paper cites.
O. Boiman, M. Irani, Detecting irregularities in images and in video, Int. J. Computer Vision 74 (1) (2007) 17–31
2007
Earlier work this paper cites.
C. Piciarelli, C. Micheloni, G. L. Foresti, Trajectory-based anomalous event detection, IEEE Trans. Circuits Systems Video Technology 18 (11) (2008) 1544–1554
2008
Earlier work this paper cites.
A. Adam, E. Rivlin, I. Shimshoni, D. Reinitz, Robust real-time unusual event detection using multiple fixed-location monitors, IEEE Trans. Pattern Analysis Machine Intelligence 30 (3) (2008) 555–560
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Int. Conf. Machine Learning, 2008, pp. 1096–1103
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in: Computer Vision Pattern Recognition, 2009, pp. 248–255
2009
Earlier work this paper cites.
P. Antonakaki, D. Kosmopoulos, S. J. Perantonis, Detecting abnormal human behaviour using multiple cameras, Signal Processing 89 (9) (2009) 1723 – 1738
2009
Earlier work this paper cites.
J. Kim, K. Grauman, Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates, in: Computer Vision Pattern Recognition, 2009, pp. 2921–2928
2009
Earlier work this paper cites.
Y. Benezeth, P. M. Jodoin, V. Saligrama, C. Rosenberger, Abnormal events detection based on spatio-temporal co-occurrences, in: Computer Vision Pattern Recognition, 2009, pp. 2458–2465
2009
Earlier work this paper cites.
L. Kratz, K. Nishino, Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models, in: Computer Vision Pattern Recognition, 2009, pp. 1446–1453
2009
Earlier work this paper cites.
R. Mehran, A. Oyama, M. Shah, Abnormal crowd behavior detection using social force model, in: Computer Vision Pattern Recognition, 2009, pp. 935–942
2009
Earlier work this paper cites.
A. Zaharescu, R. Wildes, Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing, in: European Conf. Computer Vision, vol. 1, 2010, pp. 563–576
2010
Earlier work this paper cites.
F. Jiang, J. Yuan, S. A. Tsaftaris, A. K. Katsaggelos, Anomalous video event detection using spatiotemporal context, Computer Vision Image Understanding 115 (3) (2011) 323 – 333
2011
Earlier work this paper cites.
S. Calderara, U. Heinemann, A. Prati, R. Cucchiara, N. Tishby, Detecting anomalies in people’s trajectories using spectral graph analysis, Computer Vision Image Understanding 115 (8) (2011) 1099 – 1111
2011
Earlier work this paper cites.
B. T. Morris, M. M. Trivedi, Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach, IEEE Trans. Pattern Analysis Machine Intelligence 33 (11) (2011) 2287–2301
2011
Earlier work this paper cites.
F. Tung, J. S. Zelek, D. A. Clausi, Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance, Image Vision Computing 29 (4) (2011) 230 – 240
2011
Earlier work this paper cites.
Y. Cong, J. Yuan, J. Liu, Sparse reconstruction cost for abnormal event detection, in: Computer Vision Pattern Recognition, 2011, pp. 3449–3456
2011
Earlier work this paper cites.
B. Antic, B. Ommer, Video parsing for abnormality detection, in: Int. Conf. Computer Vision, 2011, pp. 2415–2422
2011
Cited alongside, same era.
V. Reddy, C. Sanderson, B. C. Lovell, Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture, in: Computer Vision Pattern Recognition Workshops, 2011, pp. 55–61
2011
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, Advances Neural Information Processing Systems (2012) 1097–1105
2012
Cited alongside, same era.
V. Saligrama, Z. Chen, Video anomaly detection based on local statistical aggregates, in: Computer Vision Pattern Recognition, 2012, pp. 2112–2119
2012
Cited alongside, same era.
H. Ullah, N. Conci, Crowd motion segmentation and anomaly detection via multi-label optimization, in: ICPR Workshop Pattern Recognition Crowd Analysis, 2012
D. Xu, R. Song, X. Wu, N. Li, W. Feng, H. Qian Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts, Neurocomputing 143 (2014) 144–152
2014
Later among the works it cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: Convolutional architecture for fast feature embedding, in: ACM Int. Conf. Multimedia, 2014, pp. 675–678
2014
Later among the works it cites.
M. Sabokrou, M. Fathy, M. Hoseini, R. Klette, Real-time anomaly detection and localization in crowded scenes, in: Computer Vision Pattern Recognition Workshops, 2015, pp. 56–62
2015
Later among the works it cites.
2015
Later among the works it cites.
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2012
Cited alongside, same era.
2012
Cited alongside, same era.
A. Giusti, D. C. Ciresan, J. Masci, L. M. Gambardella, J. Schmidhuber, Fast image scanning with deep max-pooling convolutional neural networks, in: IEEE Int. Conf. Image Processing, 2013, pp. 4034–4038
2013
Cited alongside, same era.
C. Lu, J. Shi, J. Jia, Abnormal event detection at 150 fps in Matlab, in: Int. Conf. Computer Vision, 2013, pp. 2720–2727
2013
Cited alongside, same era.
M. Javan Roshtkhari, M. D. Levine An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions, in: Computer Vision Image Understanding 117 (10) (2013) 1436 – 1452
2013
Cited alongside, same era.
Y. Zhu, N. M. Nayak, A. K. Roy-Chowdhury, Context-aware modeling and recognition of activities in video, in: Computer Vision Pattern Recognition, 2013, pp. 2491–2498
2013
Cited alongside, same era.
Y. Cong, J. Yuan, Y. Tang, Video anomaly search in crowded scenes via spatio-temporal motion context, IEEE Trans. Information Forensics Security 8 (10) (2013) 1590–1599
2013
Cited alongside, same era.
M. J. Roshtkhari, M. D. Levine, Online dominant and anomalous behavior detection in videos, in: Computer Vision Pattern Recognition, 2013, pp. 2611–2618
2013
Cited alongside, same era.
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Computer Vision Pattern Recognition, 2015
2015
Later among the works it cites.
C. Tsung-Han, K. Jia, S. Gao, J. Lu, Z. Zeng, Y. Ma, PcaNet: A simple deep learning baseline for image classification?, IEEE Trans. Image Processing (2015) 5017–5032
2015
Later among the works it cites.
H. Mousavi, M. Nabi, H. K. Galoogahi, A. Perina, V. Murino, Abnormality detection with improved histogram of oriented tracklets in: Int. Conf. Image Analysis Processing, 2015, pp. 722–732
2015
Later among the works it cites.
Y. Yuan, J. Fang, Q. Wang, Online anomaly detection in crowd scenes via structure analysis, IEEE Trans. Cybernetics (2015) 548–561
2015
Later among the works it cites.
K. W. Cheng, Y. T. Chen, W. H. Fang, Video anomaly detection and localization using hierarchical feature representation and gaussian process regression, in: Computer Vision Pattern Recognition, 2015, pp. 2909–2917
2015
Later among the works it cites.
T. Xiao, C. Zhang, H. Zha, Learning to detect anomalies in surveillance video, IEEE Signal Processing Letters 22 (9) (2015) 1477–1481
2015
Later among the works it cites.
D. G. Lee, H. I. Suk, S. K. Park, S. W. Lee, Motion influence map for unusual human activity detection and localization in crowded scenes, IEEE Trans. Circuits Systems Video Technology 25 (10) (2015) 1612–1623
2015
Later among the works it cites.
N. Li, X. Wu, D. Xu, H. Guo, W. Feng, Spatio-temporal context analysis within video volumes for anomalous-event detection and localization, Neurocomputing 155 (2015) 309 – 319
2015
Later among the works it cites.
M. Sabokrou, M. Fathy, M. Hoseini, Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder, Electronics Letters (2016) 1122–1124
2016
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2016
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F. Zhijun, F. Fei, Y. Fang, C. Lee, N. Xiong, L. Shu, S. Chen, Abnormal event detection in crowded scenes based on deep learning, Multimedia Tools Applications (2016) 14617–14639
2016
Closest in time.
ImageNet (2017). image-net.org
2017
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MIT places database (2017). places.csail.mit.edu
2017
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F. Yachuang, Y. Yuan, L. Xiaoqiang, Learning deep event models for crowd anomaly detection, Neurocomputing (2017) 548–556
2017
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Ucsd anomaly detection dataset (2017). http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm
2017
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S. Wu, B. E. Moore, M. Shah, Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes, in: Computer Vision Pattern Recognition, 2010, pp. 2054–2060
2060
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