Road user tracker based on robust regression with GNC and preconditioning

Abstract

In this paper an early vision tracking algorithm particularly adapted to the tracking of road users in video image sequences is presented. The algorithm is an enhanced version of the regression based motion estimator in Lucas-Kanade style. Robust regression algorithms work in the presence of outliers, while one distinct property of the proposed algorithm is that it can handle with datasets including 90% outliers. Robust regression involves finding the global minimum of a cost function, where the cost function measures if the motion model is conform with the measured data. The minimization task can be addressed with the graduated non convexity (GNC) heuristics. GNC is a scale space analysis of the cost function in parameter space. Although the approach is elegant and reasonable, several attempts to use GNC for solving robust regression tasks known from literature failed in the past. The main improvement of the proposed method compared with prior approaches is the use of a preconditioning technique to avoid GNC from getting stuck in a local minimum.