Real-time Hybrid Tracking using Edge and Texture Information

This paper proposes a real-time, robust and effective tracking framework for visual servoing applications. The algorithm is based on the fusion of visual cues and on the estimation of a transformation (either a homography or a 3D pose). The parameters of this transformation are estimated using a non-linear minimization of a unique criterion that integrates information both on the texture and the edges of the tracked object. The proposed tracker is more robust and performs well in conditions where methods based on a single cue fail. The framework has been tested for 2D object motion estimation and pose computation. The method presented in this paper has been validated on several video sequences as well as in visual servoing experiments considering various objects. Results show the method to be robust to occlusions or textured backgrounds and suitable for visual servoing applications.