A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
One-class svms for document classification
The Journal of Machine Learning Research
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Semantic Event Detection using Conditional Random Fields
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Unusual Event Detection via Multi-camera Video Mining
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Analysis and query of person-vehicle interactions in homography domain
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Abnormal Event Detection in Video Using N-cut Clustering
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
Monitoring human behavior in an assistive environment using multiple views
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A hierarchical self-organizing approach for learning the patterns of motion trajectories
IEEE Transactions on Neural Networks
Non-parametric anomaly detection exploiting space-time features
Proceedings of the international conference on Multimedia
Dense spatio-temporal features for non-parametric anomaly detection and localization
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Robust workflow recognition using holistic features and outlier-tolerant fused hidden Markov models
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Multimedia Tools and Applications
Multi-scale and real-time non-parametric approach for anomaly detection and localization
Computer Vision and Image Understanding
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
Computer Vision and Image Understanding
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In this paper a bottom-up approach for human behaviour understanding is presented, using a multi-camera system. The proposed methodology, given a training set of normal data only, classifies behaviour as normal or abnormal, using two different criteria of human behaviour abnormality (short-term behaviour and trajectory of a person). Within this system an one-class support vector machine decides short-term behaviour abnormality, while we propose a methodology that lets a continuous Hidden Markov Model function as an one-class classifier for trajectories. Furthermore, an approximation algorithm, referring to the Forward Backward procedure of the continuous Hidden Markov Model, is proposed to overcome numerical stability problems in the calculation of probability of emission for very long observations. It is also shown that multiple cameras through homography estimation provide more precise position of the person, leading to more robust system performance. Experiments in an indoor environment without uniform background demonstrate the good performance of the system.