Edge-Projected Integration of Image and Model Cues for Robust Model-Based Object Tracking

Abstract

A real-world limitation of visual servoing approaches is the sensitivity of visual tracking to varying ambient conditions and background clutter. The authors present a model-based vision framework to improve the robustness of edge-based feature tracking. Lines and ellipses are tracked using edge-projected integration of cues (EPIC). EPIC uses cues in regions delineated by edges that are defined by observed edgels and a priori knowledge from a wire-frame model of the object. The edgels are then used for a robust fit of the feature geometry, but at times this results in multiple feature candidates. A final validation step uses the model topology to select the most likely feature candidates. EPIC is suited for real-time operation. Experiments demonstrate operation at frame rate. Navigating a walking robot through an industrial environment shows the robustness to varying lighting conditions. Tracking objects over varying backgrounds indicates robustness to clutter.