Automatic confidence adjustment of visual cues in model-based camera tracking

Abstract

Model-based camera tracking is a technology that estimates a precise camera pose based on visual cues (e.g., feature points, edges) extracted from camera images given a 3D scene model and a rough camera pose. This paper proposes an automatic method for flexibly adjusting the confidence of visual cues in model-based camera tracking. The adjustment is based on the conditions of the target object/scene and the reliability of the initial or previous camera pose. Under uncontrolled or less-controlled working environments, the proposed object-adaptive tracking method works flexibly at 20 frames per second on an ultra mobile personal computer (UMPC) with an average tracking error within 3 pixels when the camera image resolution is 320 by 240 pixels. This capability enabled the proposed method to be successfully applied to a mobile augmented reality (AR) guidance system for a museum.