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The paper investigates the unsupervised learning of a model of activity for a multi-camera surveillance network that can be created from a large set of observations. This enables the learning algorithm to establish links between camera views associated with an activity. The learning algorithm operates in a correspondence-free manner, exploiting the(More)
This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data. A scene model is proposed that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones. We present several unsupervised methods that learn these scene elements(More)
Identifying moving vehicles is a critical task for an urban traffic monitoring system. With static cameras, background subtraction techniques are commonly used to separate foreground moving objects from background at the pixel level. Gaussian mixture model is commonly used for background modelling. Most background modelling techniques use a single leaning(More)
Measurement of fish steroids in water provides a non-invasive alternative to measurement in blood samples, offering the following advantages: zero or minimal intervention (i.e. no anaesthetic, bleeding or handling stress); results not being biased by sampling stress; repeat measurements on the same fish; the possibility of making non-lethal measurements on(More)
Fish behaviourists are increasingly turning to non-invasive measurement of steroid hormones in holding water, as opposed to blood plasma. When some of us met at a workshop in Faro, Portugal, in September, 2007, we realised that there were still many issues concerning the application of this procedure that needed resolution, including: Why do we measure(More)
We report an investigation to determine the topology of an arbitrary network of video cameras observing an environment. The topology is learnt in an unsupervised manner by temporal correlation of objects transiting between adjacent camera viewfields. We extract this information in two steps, firstly identifying the principal entry and exit zones associated(More)
This paper presents a system for vehicle detection, tracking and classification from roadside CCTV. The system counts vehicles and separates them into four categories: car, van, bus and motorcycle (including bicycles). A new background Gaussian Mixture Model (GMM) and shadow removal method have been used to deal with sudden illumination changes and camera(More)
This paper addresses the problem of automatically extracting frequently used pedestrian pathways from video sequences of natural outdoor scenes. Path models are learnt from the accumulation of trajectory data over long time periods, and can be used to augment the classification of subsequent track data. In particular, labelled paths provide an efficient(More)