A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data-Driven Extraction of Curved Intersection Lanemarks from Road Traffic Image Sequences
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Bayesian Computer Vision System for Modeling Human Interaction
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Towards Learning Naive Physics by Visual Observation: Qualitative Spatial Representations
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Natural Language Description of Image Sequences as a Form of Knowledge Representation
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Analysis of Object Interactions in Dynamic Scenes
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Proceedings of the 5th international conference on Multimodal interfaces
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Pattern Recognition Letters
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
S-SEER: selective perception in a multimodal office activity recognition system
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
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We describ e an implemented technique for generating event models automatically based on qualitative reasoning and a statistical analysis of video input. Using an existing tracking program which generates labelled contours for objects in every frame, the view from a fixed camera is partitioned into semantically relevant regions based on the paths followed by movingobjects. The paths are indexed with temporal information so objects moving along the same path at different speeds can be distinguished. Using a notion of proximity based on the speed of the moving objects and qualitative spatial reasoning techniques, event models describing the behaviour of pairs of objects can be built, again using statistical methods. The system has been tested on a traffic domain and learns various event models expressed in the qualitative calculus which represent human observable events. The system can then be used to recognise subsequent selected event occurrences or unusual behaviours.