Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence
Fundamentals of speech recognition
Fundamentals of speech recognition
Visual surveillance in a dynamic and uncertain world
Artificial Intelligence - Special volume on computer vision
Image difference threshold strategies and shadow detection
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
A Camera-Based System for Tracking People in Real Time
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Agent Orientated Annotation in Model Based Visual Surveillance
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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A multi-agent architecture is presented for implementing scene understanding algorithms in the visual surveillance domain. To achieve a high level description of events observed by multiple cameras, many inter-related, event-driven processes must be executed. We use the agent paradigm to provide a framework in which these processes can be managed. Each camera has an associated camera agent, which detects and tracks moving events (or regions of interest). Each camera is calibrated so that image co-ordinates can be transformed into ground plane locations. Each camera agent instantiates and updates object agents for each stable image event it detects. Object agents are responsible for continually updating a 3D trajectory, a view-independent chromatic appearance model, a description of the event's behaviour, and from these a classification of the object type itself. Camera agents synchronously supply each of its associated object agents with current chromatic and 3D positional observations of the tracked events. Each object agent classifies itself from a range of predefined activities each evaluated using a trained hidden Markov model. The combination of the agent framework, and visual surveillance application provides an excellent environment for development and evaluation of scene understanding algorithms.