Modeling complex luminance variations for target tracking

Lambertpsilas model is widely used in low level computer vision algorithms such as matching, tracking or optical flow computation for example. However, it is well known that these algorithms often fail when they face complex luminance variations. Therefore, we revise in this paper the underlying hypothesis of its temporal constancy and propose a new optical flow constraint. To do that, we use the Blinn-Phong reflection model to take into account that the scene may move with respect to the lighting and/or to the observer, and that specular highlights may occur. To validate in practice these analytical results, we consider the case where a camera is mounted on a robot end-effector with a lighting mounted on this camera and show experimental results of target tracking by visual servoing. Such an approach requires to analytically compute the luminance variations due to the observer motion which can be easily derived from our revised optical flow constraint. In addition, while the visual servoing classical approaches rely on geometric features, we present here a new method that directly relies on the luminance of all pixels in the image which does not require any tracking or matching process.