Machine Learning - Special issue on learning with probabilistic representations
Convexity rule for shape decomposition based on discrete contour evolution
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
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Normalized Cuts and Image Segmentation
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The Recognition of Human Movement Using Temporal Templates
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Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Motion-Based Video Representation for Scene Change Detection
International Journal of Computer Vision
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Negotiating the Semantic Gap: From Feature Maps to Semantic Landscapes
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Relevance Ranking of Video Data using Hidden Markov Model Distances and Polygon Simplification
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Exploring Video Structure Beyond The Shots
ICMCS '98 Proceedings of the IEEE International Conference on Multimedia Computing and Systems
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognition of Group Activities using Dynamic Probabilistic Networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Video Behaviour Profiling and Abnormality Detection without Manual Labelling
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
Towards a comprehensive survey of the semantic gap in visual image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Event based indexing of broadcasted sports video by intermodalcollaboration
IEEE Transactions on Multimedia
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
Unsupervised Real-Time Unusual Behavior Detection for Biometric-Assisted Visual Surveillance
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Motion trajectory reproduction from generalized signature description
Pattern Recognition
Relating "Pace' to Activity Changes in Mono- and Multi-camera Surveillance Videos
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Human action recognition using boosted EigenActions
Image and Vision Computing
Detecting and discriminating behavioural anomalies
Pattern Recognition
Video Behaviour Mining Using a Dynamic Topic Model
International Journal of Computer Vision
Incremental behavior modeling and suspicious activity detection
Pattern Recognition
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This paper tackles the problem of surveillance video content modelling. Given a set of surveillance videos, the aims of our work are twofold: firstly a continuous video is segmented according to the activities captured in the video; secondly a model is constructed for the video content, based on which an unseen activity pattern can be recognised and any unusual activities can be detected. To segment a video based on activity, we propose a semantically meaningful video content representation method and two segmentation algorithms, one being offline offering high accuracy in segmentation, and the other being online enabling real-time performance. Our video content representation method is based on automatically detected visual events (i.e. 'what is happening in the scene'). This is in contrast to most previous approaches which represent video content at the signal level using image features such as colour, motion and texture. Our segmentation algorithms are based on detecting breakpoints on a high-dimensional video content trajectory. This differs from most previous approaches which are based on shot change detection and shot grouping. Having segmented continuous surveillance videos based on activity, the activity patterns contained in the video segments are grouped into activity classes and a composite video content model is constructed which is capable of generalising from a small training set to accommodate variations in unseen activity patterns. A run-time accumulative unusual activity measure is introduced to detect unusual behaviour while usual activity patterns are recognised based on an online likelihood ratio test (LRT) method. This ensures robust and reliable activity recognition and unusual activity detection at the shortest possible time once sufficient visual evidence has become available. Comparative experiments have been carried out using over 10h of challenging outdoor surveillance video footages to evaluate the proposed segmentation algorithms and modelling approach.