In this thesis, we develop an algorithm for detecting independent motion in real-time from 2D image sequences of arbitrarily complex 3D scenes. We discuss the necessary background information in image formation, optical flow, multiple view geometry, robust estimation, and real-time camera and scene pose estimation for constructing and understanding the operation of our algorithm. Furthermore, we provide an overview of existing independent motion detection techniques and compare them to our proposed solution. Unfortunately, the existing independent motion detection techniques were not evaluated quantitatively nor were their source code made publicly available. Therefore, it is not possible to make direct comparisons. Instead, we constructed several comparison algorithms which should have comparable performance to these previous approaches. We developed methods for quantitatively comparing independent motion detection algorithms and found that our solution had the best performance. By establishing a method for quantitatively evaluating these algorithms and publishing our results, we hope to foster better research in this area and help future investigators more quickly advance the state of the art. / text

Autonomous robot navigation in dynamic environments requires robust detection of egomotion and independent motion. This thesis introduces a novel solution to the problem of visual independent motion detection by interpreting the topological features of omnidirectional dense optical flow field and determining the background - egomotion direction. The thesis solves the problem of visual independent motion detection in four interdependent subtasks. Independent Motion Detection can only be accomplished if the egomotion detection yields a relevant background motion model. Therefore, the problem of Egomotion Detection is solved first by exploiting the topological structures of the global omnidirectional optical flow fields. The estimation of the optical flow field is the prerequisite of the Egomotion-Detection task. Since the omnidirectional projection introduces non-affine deformations on the image plane, the known optical flow calculation methods have to be modified to yield accurate results. This modification is introduced here as another subtask, Omnidirectional Optical Flow Estimation. The experiments concerning the 3D omnidirectional scene capturing are grouped under the fourth subtask 3D Omni-Image Processing.