An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders

[1]  Heitor Silvério Lopes,et al.  A study of deep convolutional auto-encoders for anomaly detection in videos , 2018, Pattern Recognit. Lett..

[2]  David A. Clausi,et al.  Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance , 2011, Image Vis. Comput..

[3]  Junsong Yuan,et al.  Sparse reconstruction cost for abnormal event detection , 2011, CVPR 2011.

[4]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[5]  Mahmood Fathy,et al.  Real-time anomaly detection and localization in crowded scenes , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[6]  Shenghua Gao,et al.  Future Frame Prediction for Anomaly Detection - A New Baseline , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[7]  Norman I. Badler,et al.  Detection of Global and Local Motion Changes in Human Crowds , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Nicu Sebe,et al.  Detecting anomalous events in videos by learning deep representations of appearance and motion , 2017, Comput. Vis. Image Underst..

[9]  Alberto Del Bimbo,et al.  Multi-scale and real-time non-parametric approach for anomaly detection and localization , 2012, Comput. Vis. Image Underst..

[10]  Nannan Li,et al.  Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts , 2014, Neurocomputing.

[11]  Marimuthu Palaniswami,et al.  A visual-numeric approach to clustering and anomaly detection for trajectory data , 2017, The Visual Computer.

[12]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Xiaoqiang Lu,et al.  Learning deep event models for crowd anomaly detection , 2017, Neurocomputing.

[14]  R. Venkatesh Babu,et al.  Anomaly detection via short local trajectories , 2017, Neurocomputing.

[15]  S. SowmyaKamath,et al.  Dynamic video anomaly detection and localization using sparse denoising autoencoders , 2017, Multimedia Tools and Applications.

[16]  Ehud Rivlin,et al.  Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Cewu Lu,et al.  Abnormal Event Detection at 150 FPS in MATLAB , 2013, 2013 IEEE International Conference on Computer Vision.

[18]  Hongbin Zha,et al.  Learning to Detect Anomalies in Surveillance Video , 2015, IEEE Signal Processing Letters.

[19]  Jonghyun Choi,et al.  Learning Temporal Regularity in Video Sequences , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[21]  Venkatesh Saligrama,et al.  Video anomaly detection based on local statistical aggregates , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Mahmood Fathy,et al.  Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes , 2017, IEEE Transactions on Image Processing.

[23]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[24]  Huchuan Lu,et al.  Video anomaly detection based on locality sensitive hashing filters , 2016, Pattern Recognit..

[25]  Nuno Vasconcelos,et al.  Anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Ramin Mehran,et al.  Abnormal crowd behavior detection using social force model , 2009, CVPR.

[27]  Hong Liu,et al.  Online growing neural gas for anomaly detection in changing surveillance scenes , 2017, Pattern Recognit..

[28]  Louis Kratz,et al.  Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models , 2009, CVPR.

[29]  Dhananjay Kumar,et al.  An efficient system for anomaly detection using deep learning classifier , 2017, Signal Image Video Process..

[30]  Nuno Vasconcelos,et al.  Anomaly Detection and Localization in Crowded Scenes , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  W. Gunawan,et al.  A review on classifying abnormal behavior in crowd scene , 2019, J. Vis. Commun. Image Represent..

[32]  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.

[33]  William Robson Schwartz,et al.  Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[34]  Kristen Grauman,et al.  Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates , 2009, CVPR.

[35]  Xuelong Li,et al.  Collective Representation for Abnormal Event Detection , 2017, Journal of Computer Science and Technology.

[36]  Quansen Sun,et al.  A Content-Adaptively Sparse Reconstruction Method for Abnormal Events Detection With Low-Rank Property , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[37]  Vikas Gupta,et al.  Abnormality detection in crowd videos by tracking sparse components , 2017, Machine Vision and Applications.

[38]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[39]  Mubarak Shah,et al.  Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[40]  Mahmood Fathy,et al.  Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes , 2016, Comput. Vis. Image Underst..