Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

This paper highlights the role of ground manifold modeling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single-disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different ground-manifold models (e.g., plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. This paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results, compared with the binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. The experimental results also include quantitative evaluations of the tradeoff between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions

3D Motion Analysis via Energy Minimization

This work deals with 3D motion analysis from stereo image sequences for driver assistance systems. It consists of two parts: the estimation of motion from the image data and the segmentation of moving objects in the input images. The content can be summarized with the technical term machine visual kinesthesia, the sensation or perception and cognition of motion. In the first three chapters, the importance of motion information is discussed for driver assistance systems, for machine vision in general, and for the estimation of ego motion. The next two chapters delineate on motion perception, analyzing the apparent movement of pixels in image sequences for both a monocular and binocular camera setup. Then, the obtained motion information is used to segment moving objects in the input video. Thus, one can clearly identify the thread from analyzing the input images to describing the input images by means of stationary and moving objects. Finally, I present possibilities for future applications based on the contents of this thesis. Previous work in each case is presented in the respective chapters. Although the overarching issue of motion estimation from image sequences is related to practice, there is nothing as practical as a good theory (Kurt Lewin). Several problems in computer vision are formulated as intricate energy minimization problems. In this thesis, motion analysis in image sequences is thoroughly investigated, showing that splitting an original complex problem into simplified sub-problems yields improved accuracy, increased robustness, and a clear and accessible approach to state-of-the-art motion estimation techniques. In Chapter 4, optical flow is considered. Optical flow is commonly estimated by minimizing the combined energy, consisting of a data term and a smoothness term. These two parts are decoupled, yielding a novel and iterative approach to optical flow. The derived Refinement Optical Flow framework is a clear and straight-forward approach to computing the apparent image motion vector field. Furthermore this results currently in the most accurate motion estimation techniques in literature. Much as this is an engineering approach of fine-tuning precision to the last detail, it helps to get a better insight into the problem of motion estimation. This profoundly contributes to state-of-the-art research in motion analysis, in particular facilitating the use of motion estimation in a wide range of applications. In Chapter 5, scene flow is rethought. Scene flow stands for the three-dimensional motion vector field for every image pixel, computed from a stereo image sequence. Again, decoupling of the commonly coupled approach of estimating three-dimensional position and three dimensional motion yields an approach to scene ow estimation with more accurate results and a considerably lower computational load. It results in a dense scene flow field and enables additional applications based on the dense three-dimensional motion vector field, which are to be investigated in the future. One such application is the segmentation of moving objects in an image sequence. Detecting moving objects within the scene is one of the most important features to extract in image sequences from a dynamic environment. This is presented in Chapter 6. Scene flow and the segmentation of independently moving objects are only first steps towards machine visual kinesthesia. Throughout this work, I present possible future work to improve the estimation of optical flow and scene flow. Chapter 7 additionally presents an outlook on future research for driver assistance applications. But there is much more to the full understanding of the three-dimensional dynamic scene. This work is meant to inspire the reader to think outside the box and contribute to the vision of building perceiving machines.</em

Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications

Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications

Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

MOBILE ROBOT SELF-PLANNING AND NAVIGATION BASED ON ARTIFICIAL LANDMARK LOCALIZATION METHOD AND BINOCULAR STEREO VISION

This paper describes design of artificial landmark based on colour model used for Self-planning in unstructured environment to a robot for its movement. This method provides less error in estimation when compared to existing methods. This project is an investigation into building a system which visually detects artificial landmarks to determine the landmarks within a location, decipher their position within that location and track the landmark throughout the location using Binocular stereo vision