Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
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Nicu Sebe | Dan Xu | Elisa Ricci | Jingkuan Song | Yan Yan | N. Sebe | E. Ricci | Jingkuan Song | Dan Xu | Yan Yan
[1] Mubarak Shah,et al. Abnormal crowd behavior detection using social force model , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Junsong Yuan,et al. Sparse reconstruction cost for abnormal event detection , 2011, CVPR 2011.
[3] Brian C. Lovell,et al. Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture , 2011, CVPR 2011 WORKSHOPS.
[4] Nicu Sebe,et al. Multi-task linear discriminant analysis for multi-view action recognition , 2013, 2013 IEEE International Conference on Image Processing.
[5] Christophe Rosenberger,et al. Abnormal events detection based on spatio-temporal co-occurences , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[6] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[7] Dit-Yan Yeung,et al. Learning a Deep Compact Image Representation for Visual Tracking , 2013, NIPS.
[8] W. Eric L. Grimson,et al. Learning Semantic Scene Models by Trajectory Analysis , 2006, ECCV.
[9] Subramanian Ramanathan,et al. No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion , 2013, 2013 IEEE International Conference on Computer Vision.
[10] Venkatesh Saligrama,et al. Video anomaly detection based on local statistical aggregates , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[11] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[12] Shiguang Shan,et al. Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment , 2014, ECCV.
[13] Nitish Srivastava,et al. Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..
[14] Andrew Zisserman,et al. Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.
[15] Luc Van Gool,et al. Visual interestingness in image sequences , 2013, MM '13.
[16] Juhan Nam,et al. Multimodal Deep Learning , 2011, ICML.
[17] K. Grauman,et al. Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Louis Kratz,et al. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models , 2009, CVPR.
[19] Nicu Sebe,et al. Detecting anomalous events in videos by learning deep representations of appearance and motion , 2017, Comput. Vis. Image Underst..
[20] Cewu Lu,et al. Abnormal Event Detection at 150 FPS in MATLAB , 2013, 2013 IEEE International Conference on Computer Vision.
[21] Mubarak Shah,et al. Visual attention detection in video sequences using spatiotemporal cues , 2006, MM '06.
[22] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[23] Tieniu Tan,et al. Similarity based vehicle trajectory clustering and anomaly detection , 2005, IEEE International Conference on Image Processing 2005.
[24] L. Kratz,et al. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[26] Ehud Rivlin,et al. Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Nuno Vasconcelos,et al. Anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[28] Gian Luca Foresti,et al. Trajectory-Based Anomalous Event Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.
[29] Ce Liu,et al. Exploring new representations and applications for motion analysis , 2009 .
[30] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[31] G. Zoutendijk,et al. Methods of feasible directions : a study in linear and non-linear programming , 1960 .
[32] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[33] Martin D. Levine,et al. Online Dominant and Anomalous Behavior Detection in Videos , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Andrei Zaharescu,et al. Anomalous Behaviour Detection Using Spatiotemporal Oriented Energies, Subset Inclusion Histogram Comparison and Event-Driven Processing , 2010, ECCV.
[35] Shaogang Gong,et al. Video Behaviour Mining Using a Dynamic Topic Model , 2011, International Journal of Computer Vision.
[36] Venkatesh Saligrama,et al. Modeling background activity for behavior subtraction , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.
[37] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[38] Wei Liu,et al. Double Fusion for Multimedia Event Detection , 2012, MMM.
[39] Nicu Sebe,et al. A Prototype Learning Framework Using EMD: Application to Complex Scenes Analysis , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Rama Chellappa,et al. "Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection , 2005, IEEE Transactions on Image Processing.
[41] W. Eric L. Grimson,et al. Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..