Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Recognize Visual Dynamic Events from Examples
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Pruning Training Sets for Learning of Object Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear-Time Computation of Similarity Measures for Sequential Data
The Journal of Machine Learning Research
Spectral Clustering and Embedding with Hidden Markov Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
Incremental spectral clustering by efficiently updating the eigen-system
Pattern Recognition
On evolutionary spectral clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Clustering of time series data-a survey
Pattern Recognition
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models
IEEE Transactions on Image Processing
Multifeature Object Trajectory Clustering for Video Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Trajectory-Based Anomalous Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
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This paper addresses the problem of automatically learning common behaviors from long time observations of a scene of interest, with the purpose of classifying actions and, possibly, detecting anomalies. Unsupervised learning is used as an effective way to extract information from the scene with a very limited intervention of the user. The method we propose is rather general, but fits very naturally to a videosurveillance scenario, where the same environment is observed for a long time, usually from a distance. The experimental analysis is based on thousands of dynamic events acquired by three-weeks observations of a single-camera video-surveillance system installed in our department.