Proceedings of the ACM International Conference on Image and Video Retrieval
Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding
International Journal of Computer Vision
Detecting and discriminating behavioural anomalies
Pattern Recognition
Cloud-computing-based framework for multi-camera topology inference in smart city sensing system
Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing
Video topic modelling with behavioural segmentation
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Automatic learning of background semantics in generic surveilled scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Exploiting multiple cameras for environmental pathlets
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Automatic workflow monitoring in industrial environments
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Motion pattern extraction and event detection for automatic visual surveillance
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
International Journal of Computer Vision
Detection of similar sequences in EEG maps series using correlation coefficients matrix
Machine Graphics & Vision International Journal
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
Dynamic scene understanding by improved sparse topical coding
Pattern Recognition
Summarizing high-level scene behavior
Machine Vision and Applications
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This paper presents a novel framework for detecting abnormal pedestrian and vehicle behaviour by modelling cross-correlation among different co-occurring objects both locally and globally in a given scene. We address this problem by first segmenting a scene into semantic regions according to how object events occur globally in the scene, and second modelling concurrent correlations among regional object events both locally (within the same region) and globally (across different regions). Instead of tracking objects, the model represents behaviour based on classification of atomic video events, designed to be more suitable for analysing crowded scenes. The proposed system works in an unsupervised manner throughout using automatic model order selection to estimate its parameters given video data of a scene for a brief training period. We demonstrate the effectiveness of this system with experiments on public road traffic data.