We present a method for estimating the 3D shape of an object from a sequence of images captured by a hand-held device. The method is well suited to augmented reality applications in that minimal user interaction is required, and the models generated are of an appropriate form. The method proceeds by segmenting the object in every image as it is captured and using the calculated silhouette to update the current shape estimate. In contrast to previous silhouette-based modelling approaches, however, the segmentation process is informed by a 3D prior based on the previous shape estimate. A voting scheme is also introduced in order to compensate for the inevitable noise in the camera position estimates. The combination of the voting scheme with the closed-loop segmentation process provides a robust and flexible shape estimation method. We demonstrate the approach on a number of scenes where segmentation without a 3D prior would be challenging.

An accurate estimate of camera overlap is a key enabler for efficient network-wide surveillance processing (e.g. inter-camera tracking), especially in large-scale surveillance networks. Techniques based on contradictions in pair-wise occupancy data, such as the exclusion approach, have advantages in robustness and efficiency that make them particularly well suited for large surveillance networks. Correlation techniques share some of these advantages,but have a better understood statistical basis. This paper evaluates a set of contradiction and correlation techniques, using a novel metric, search space precision-recall. This metric reflects the activity-based overlap estimation required for camera handover, such as would be used in inter-camera tracking.Results are reported for a range of networks, including a 24-camera network setup in an office space, where the exclusion estimator showed the best performance.

We present an interactive image-based modelling method for generating 3D models within an augmented reality system. Applying real time camera tracking, and high-level automated image analysis, enables more powerful modelling interactions than have previously been possible. The result is an immersive modelling process which generates accurate three dimensional models of real objects efficiently and effectively. In demonstrating the modelling process on a range of indoor and outdoor scenes, we show the flexibility it offers in enabling augmented reality applications in previously unseen environments.

Automatic placement of surveillance cameras in arbitrary buildings is a challenging task, and also one that is essential for efficient deployment of large scale surveillance networks. Existing approaches for automatic camera placement are either limited to a small number of cameras, or constrained in terms of the building layouts to which they can be applied. This paper describes a new method for determining the best placement for large numbers of cameras within arbitrary building layouts. The method takes as input a 3D model of the building, and uses a genetic algorithm to find a placement that optimises coverage and (if desired) overlap between cameras. Results are reported for an implementation of the method, including its application to a wide variety of complex buildings, both real and synthetic.

Estimating the paths that moving objects can take through the fields of view of possibly non-overlapping cameras, also known as their activity topology, is an important step in the effective interpretation of surveillance video. Existing approaches to this problem involve tracking moving objects within cameras, and then attempting to link tracks across views. In contrast we propose an approach which begins by assuming all camera views are potentially linked, and successively eliminates camera topologies that are contradicted by observed motion. Over time, the true patterns of motion emerge as those which are not contradicted by the evidence. These patterns may then be used to initialise a finer level search using other approaches if required. This method thus represents an efficient and effective way to learn activity topology for a large network of cameras, particularly with a limited amount of data.

Camera calibration requires the identification of points in an image that correspond to known locations in the scene. These are typically determined through the use of a calibration pattern designed to facilitate feature localisation. We present in this paper a novel method of generating patterns such that each subregion is individually identifiable by its cross ratio. The method aims to minimise the probability of misidentifying a subregion. A key advantage of the method is the ability to place constraints on the size of the elements constituting the pattern. This allows a calibration object to be used in a wider variety of viewing conditions, increasing the flexibility of the calibration process.