This thesis proposes a multi-camera active-vision reconfiguration system which selects camera poses on-line to improve the shape recovery of a priori unknown, markerless, deforming objects in dynamic environments. The objectives of shape recovery are defined as surface sampling accuracy, and shape completeness. The completeness objective is generalized for both solid and surface-based objects as the maximization of surface visibility. Thus, improving the recovered shape of target objects is shown to be analogous to maximizing the surface area visibility. This improvement is achieved through the on-line reconfiguration of multiple cameras. The system developed herein is composed of a shape recovery method, a robust tracking algorithm, and a multi-camera reconfiguration method.

The shape recovery method is based on a modular fusion technique that produces a complete 3D mesh-model of the target object. The method fuses triangulation data with a visual hull to maximize recovery accuracy. The modularity of the method lies in the ability to modify data filtering techniques to improve modelling accuracy, and interchange stereo correspondence features. The adaptive particle filtering algorithm produces a deformation estimation of the recovered model from the tracking data. The algorithm automatically adapts to the quantity of tracking data available and changes in the objectâ s dynamics. The modularity of the algorithm allows modifications in terms of the number of particles and motion models for task-specific implementations. The reconfiguration method consists of a robust stereo-visibility objective function, workspace discretization, and a path planner. The complete system can improve the shape recovery of a priori unknown deforming objects in obstacle-laden environments when compared to static camera methods.

To validate the proposed system, extensive simulations and experiments were conducted. The simulations tested the system in comparison to static multi-camera systems, and ideal camera placement where the object model was a priori known and the cameraâ s dynamics were unconstrained. Simulation results showed the proposed methodology outperformed static cameras and approached the performance of ideal camera placement in a dynamic, obstacle-laden environment. The experimental results showed similar improvement in shape recovery when comparison to a static camera system in an obstacle-laden environment.