Using Robust Estimation Algorithms for Tracking Explicit Curves

The context of this work is lateral vehicle control using a camera as a sensor. A natural tool for controlling a vehicle is recursive filtering. The well-known Kalman filtering theory relies on Gaussian assumptions on both the state and measure random variables. However, image processing algorithms yield measurements that, most of the time, are far from Gaussian, as experimentally shown on real data in our application. It is therefore necessary to make the approach more robust, leading to the so-called robust Kalman filtering. In this paper, we review this approach from a very global point of view, adopting a constrained least squares approach, which is very similar to the half-quadratic theory, and justifies the use of iterative reweighted least squares algorithms. A key issue in robust Kalman filtering is the choice of the prediction error covariance matrix. Unlike in the Gaussian case, its computation is not straightforward in the robust case, due to the nonlinearity of th...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Jean-Philippe Tarel, Sio-Song Ieng, Pierre Charbonnier
• Keywords: Algebraic Curves, Computer Vision, Curve Fitting, ECCV 2002, Robust Estimation, Tracking