Stixmentation : from Stixels to objects

  • Already today modern driver assistance systems contribute more and more to make individual mobility in road traffic safer and more comfortable. For this purpose, modern vehicles are equipped with a multitude of sensors and actuators which perceive, interpret and react to the environment of the vehicle. In order to reach the next set of goals along this path, for example to be able to assist the driver in increasingly complex situations or to reach a higher degree of autonomy of driver assistance systems, a detailed understanding of the vehicle environment and especially of other moving traffic participants is necessary. It is known that motion information plays a key role for human object recognition [Spelke, 1990]. However, full 3D motion information is mostly not taken into account for Stereo Vision-based object segmentation in literature. In this thesis, novel approaches for motion-based object segmentation of stereo image sequences are proposed from which a generic environmental model is derived that contributes to a more precise analysis and understanding of the respective traffic scene. The aim of the environmental model is to yield a minimal scene description in terms of a few moving objects and stationary background such as houses, crash barriers or parking vehicles. A minimal scene description aggregates as much information as possible and it is characterized by its stability, precision and efficiency. Instead of dense stereo and optical flow information, the proposed object segmentation builds on the so-called Stixel World, an efficient superpixel-like representation of space-time stereo data. As it turns out this step substantially increases stability of the segmentation and it reduces the computational time by several orders of magnitude, thus enabling real-time automotive use in the first place. Besides the efficient, real-time capable optimization, the object segmentation has to be able to cope with significant noise which is due to the measurement principle of the used stereo camera system. For that reason, in order to obtain an optimal solution under the given extreme conditions, the segmentation task is formulated as a Bayesian optimization problem which allows to incorporate regularizing prior knowledge and redundancies into the object segmentation. Object segmentation as it is discussed here means unsupervised segmentation since typically the number of objects in the scene and their individual object parameters are not known in advance. This information has to be estimated from the input data as well. For inference, two approaches with their individual pros and cons are proposed, evaluated and compared. The first approach is based on dynamic programming. The key advantage of this approach is the possibility to take into account non-local priors such as shape or object size information which is impossible or which is prohibitively expensive with more local, conventional graph optimization approaches such as graphcut or belief propagation. In the first instance, the Dynamic Programming approach is limited to one-dimensional data structures, in this case to the first Stixel row. A possible extension to capture multiple Stixel rows is discussed at the end of this thesis. Further novel contributions include a special outlier concept to handle gross stereo errors associated with so-called stereo tear-off edges. Additionally, object-object interactions are taken into account by explicitly modeling object occlusions. These extensions prove to be dramatic improvements in practice. This first approach is compared with a second approach that is based on an alternating optimization of the Stixel segmentation and of the relevant object parameters in an expectation maximization (EM) sense. The labeling step is performed by means of the _−expansion graphcut algorithm, the parameter estimation step is done via one-dimensional sampling and multidimensional gradient descent. By using the Stixel World and due to an efficient implementation, one step of the optimization only takes about one millisecond on a standard single CPU core. To the knowledge of the author, at the time of development there was no faster global optimization in a demonstrator car. For both approaches, various testing scenarios have been carefully selected and allow to examine the proposed methods thoroughly under different real-world conditions with limited groundtruth at hand. As an additional innovative application, the first approach was successfully implemented in a demonstrator car that drove the so-called Bertha Benz Memorial Route from Mannheim to Pforzheim autonomously in real traffic. At the end of this thesis, the limits of the proposed systems are discussed and a prospect on possible future work is given.

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Metadaten
Author:Friedrich Philipp Joachim Erich Erbs
URN:urn:nbn:de:hebis:30:3-421092
Place of publication:Frankfurt am Main
Referee:Rudolf MesterORCiD, Jochen TrieschORCiD
Document Type:Doctoral Thesis
Language:English
Date of Publication (online):2016/11/16
Year of first Publication:2016
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Granting Institution:Johann Wolfgang Goethe-Universität
Date of final exam:2016/11/02
Release Date:2016/11/16
Page Number:182
HeBIS-PPN:395868327
Institutes:Informatik und Mathematik / Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht