Tracking line segments

Abstract

This paper describes the development and the implementation of a line segments based token tracker. Given a sequence of time-varying images, the goal is to track line segments corresponding to the edges extracted from the image being analyzed. We will present a tracking approach that combines a prediction and a matching steps. The prediction step is a Kalman filtering based approach that is used in order to provide reasonable estimates of the region where the matching process has to seek for a possible match between tokens. Correspondence in the search area is done through the use of a similarity function based on Mahalanobis distance between attributs carefully chosen of the line segments. The efficiency of the proposed approach will be illustrated in several experiments that have been carried out considering noisy synthetic data and real scenes obtained from the INRIA mobile robot.