Machine Vision and Applications
Crowd Behavior Recognition for Video Surveillance
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Self-Organizing Maps for the Automatic Interpretation of Crowd Dynamics
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Covariance Matrices for Crowd Behaviour Monitoring on the Escalator Exits
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM
IEICE - Transactions on Information and Systems
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
A Simple Method for Eccentric Event Espial Using Mahalanobis Metric
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Data-driven animation of crowds
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Abnormal crowd motion analysis
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
A proposal for local and global human activities identification
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A streakline representation of flow in crowded scenes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Motion-based unusual event detection in human crowds
Journal of Visual Communication and Image Representation
Generative group activity analysis with quaternion descriptor
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Learning video manifold for segmenting crowd events and abnormality detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Motion pattern extraction and event detection for automatic visual surveillance
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
A comprehensive study of visual event computing
Multimedia Tools and Applications
Visual crowd surveillance through a hydrodynamics lens
Communications of the ACM
An entropy approach for abnormal activities detection in video streams
Pattern Recognition
Crowd behavior surveillance using bhattacharyya distance metric
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Crowd flow characterization with optimal control theory
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Abnormal event detection in crowded scenes using sparse representation
Pattern Recognition
Multi-part sparse representation in random crowded scenes tracking
Pattern Recognition Letters
Recognizing Human Group Behaviors with Multi-group Causalities
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
A rule-based event detection system for real-life underwater domain
Machine Vision and Applications
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This work presents an automatic technique for detection of abnormal events in crowds. Crowd behaviour is difficult to predict and might not be easily semantically translated. Moreover it is difficulty to track individuals in the crowd using state of the art tracking algorithms. Therefore we characterise crowd behaviour by observing the crowd optical flow and use unsupervised feature extraction to encode normal crowd behaviour. The unsupervised feature extraction applies spectral clustering to find the optimal number of models to represent normal motion patterns. The motion models are HMMs to cope with the variable number of motion samples that might be present in each observation window. The results on simulated crowds demonstrate the effectiveness of the approach for detecting crowd emergency scenarios.