International Journal of Computer Vision
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
The evolution of video surveillance: an overview
Machine Vision and Applications
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Detecting abnormal human behaviour using multiple cameras
Signal Processing
Towards Generic Detection of Unusual Events in Video Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A dynamic hierarchical clustering method for trajectory-based unusual video event detection
IEEE Transactions on Image Processing
Video Surveillance Online Repository (ViSOR): an integrated framework
Multimedia Tools and Applications
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
Unsupervised fast anomaly detection in crowds
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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In this paper a real-time anomaly detection system for video streams is proposed. Spatio-temporal features are exploited to capture scene dynamic statistics together with appearance. Anomaly detection is performed in a non-parametric fashion, evaluating directly local descriptor statistics. A method to update scene statistics, to cope with scene changes that typically happen in real world settings, is also provided. The proposed method is tested on publicly available datasets.