Accurate and Model-Free Pose Estimation of Small Objects for Crash Video Analysis

We propose a novel model-free pose estimation algorithm to estimate the relative pose of a rigid object. In most pose estimation algorithms, the object of interest covers a large portion of the image. We focus on pose estimation of small objects covering a field of view of less than 5◦ by 5◦ using stereo vision. With this new algorithm suitable for small objects, we investigate the effect of the object size on the pose accuracy. In addition, we introduce an object tracking technique that is insensitive to partial occlusion. The main application for this method is the analysis of crash video sequences. In this context, high-speed cameras with high resolution are used. However, the method easily extends to real-time robotics applications with VGA-size images, where relative pose estimation is needed, e.g. for manipulator control.