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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)
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 investigates the combination of spatial and probabilistic models for reasoning about pedestrian behaviour in visual surveillance systems. Models are learnt by a multi-step unsupervised method and they are used for trajectory labelling and atypical behaviour detection.
This paper describes a novel application of Fourier Descriptor techniques for the recognition of hand gesture trajectories. Appearance based coordinates of hand centroids and time steps are normalized to a fixed length by multirate techniques. Fourier techniques are applied to the data to produce frequency domain data that is scale and translation… (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)
This paper proposes an activity-based semantic model for a scene under visual surveillance. It illustrates methods that allow unsupervised learning of the model, from trajectory data derived from automatic visual surveillance cameras. Results are shown for each method. Finally, the benefits of such a model in a visual surveillance system are discussed.
This paper investigates the task of identifying frequently-used pathways from video sequences of natural outdoor scenes. Path models are adaptively learnt from the accumulation of trajectory data over many image frames. Labelled paths are used as an efficient means for compressing the trajectory data for logging purposes. In addition, the path models are… (More)