Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
The Journal of Machine Learning Research
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
Model Selection for Unsupervised Learning of Visual Context
International Journal of Computer Vision
International Journal of Computer Vision
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Occlusion analysis: Learning and utilising depth maps in object tracking
Image and Vision Computing
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Segmentation for Behaviour Correlation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation methods for topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Correspondence-Free Activity Analysis and Scene Modeling in Multiple Camera Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video topic modelling with behavioural segmentation
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Exploiting sparse representations for robust analysis of noisy complex video scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Cross-domain traffic scene understanding by motion model transfer
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Computer Vision and Image Understanding
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We propose a novel framework for automatic discovering and learning of behavioural context for video-based complex behaviour recognition and anomaly detection. Our work differs from most previous efforts on learning visual context in that our model learns multi-scale spatio-temporal rather than static context. Specifically three types of behavioural context are investigated: behaviour spatial context, behaviour correlation context, and behaviour temporal context. To that end, the proposed framework consists of an activity-based semantic scene segmentation model for learning behaviour spatial context, and a cascaded probabilistic topic model for learning both behaviour correlation context and behaviour temporal context at multiple scales. These behaviour context models are deployed for recognising non-exaggerated multi-object interactive and co-existence behaviours in public spaces. In particular, we develop a method for detecting subtle behavioural anomalies against the learned context. The effectiveness of the proposed approach is validated by extensive experiments carried out using data captured from complex and crowded outdoor scenes.