Tracking simplified shapes using a stochastic boundary

When tracking extended objects, it is often the case that the shape of the target cannot be fully observed due to issues of visibility, artifacts, or high noise, which can change with time. In these situations, it is a common approach to model targets as simpler shapes instead, such as ellipsoids or cylinders. However, these simplifications cause information loss from the original shape, which could be used to improve the estimation results. In this paper, we propose a way to recover information from these lost details in the form of a stochastic boundary, whose parameters can be dynamically estimated from received measurements. The benefits of this approach are evaluated by tracking an object using noisy, real-life RGBD data.