Motion detection from temporally integrated images

Abstract

Motion blur arises when motion is fast relative to the shutter time of a camera. Unlike most work on motion blur, which considers the streaks due to motion blur to be noisy artifacts. In this paper we introduce a new method to extract motion information from these streaks. Previous methods with similar goals first extract an optic flow field from local information in the motion streaks and then infer global motion parameters. On the contrary, we adopt a more direct feature-based approach and extract global motion parameters from the motion streaks. We first extract edges in the motion blurred images, which we then group to determine the foci of expansion, the center of rotation, or motion parallel to the image plane. Furthermore, we determine the direction of motion. We present results on real images from a mobile robot in cluttered environments.