Sample-Based 3D Tracking of Colored Objects : A Flexible Architecture

This paper presents a method for 3D model-based tracking of colored objects using a sampling methodology. The problem is formulated in a Monte Carlo filtering approach, whereby the state of an object is represented by a set of hypotheses. The main originality of this work is an observation model consisting in the comparison of the color information in some sampling points around the target’s hypothetical edges. On the contrary to existing approaches the method does not need to explicitly compute edges in the video stream, thus dealing well with optical or motion blur. The method does not require the projection of the full 3D object on the image, but just of some selected points around the target’s boundaries. This allows a flexible and modular architecture illustrated by experiments performed with different objects (balls and boxes), camera models (perspective, catadioptric, dioptric) and tracking methodologies (particle and Kalman filtering).