Christos Tzomakas

AN IMAGE PROCESSING SYSTEM FOR DRIVER ASSISTANCE

In this paper, the authors describe a system designed to extract information from an image acquired from an onboard CCD camera. The purpose of the system is to detect, track and classify objects. An approach that involves integration and fusion in the sequential and parallel phases of sensor and information processing is described. The authors note that the primary advantage of this approach is the integrative coupling of different algorithms providing partly redundant information.

Walking pedestrian recognition

In recent years a lot of methods providing the ability to recognize rigid obstacles-like sedans and trucks have been developed. These methods mainly provide driving relevant information to the driver. They are able to cope reliably with scenarios on motor-ways. Nevertheless, not much attention has been given to image processing approaches to increase safety of pedestrians in traffic environments. In this paper a method for detection, tracking, and final classification of pedestrians crossing the moving observers trajectory is suggested. Herein a combination of data and model driven approaches is realized. The initial detection process is based on a texture analysis and a model-based grouping of most likely geometric features belonging to a pedestrian on intensity images. Additionally, motion patterns of limb movements are analyzed to determine initial object hypotheses. For this tracking of the quasi-rigid part of the body is performed by different trackers that have been successfully employed for tracking of sedans, trucks, motor-bikes, and pedestrians. The final classification is obtained by a temporal analysis of the walking process.

Computer vision for driver assistance systems

Systems for automated image analysis are useful for a variety of tasks and their importance is still increasing due to technological advances and an increase of social acceptance. Especially in the field of driver assistance systems the progress in science has reached a level of high performance. Fully or partly autonomously guided vehicles, particularly for road-based traffic, pose high demands on the development of reliable algorithms due to the conditions imposed by natural environments. At the Institut fur Neuroinformatik, methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We introduce a system which extracts the important information from an image taken by a CCD camera installed at the rear view mirror in a car. The approach consists of a sequential and a parallel sensor and information processing. Three main tasks namely the initial segmentation (object detection), the object tracking and the object classification are realized by integration in the sequential branch and by fusion in the parallel branch. The main gain of this approach is given by the integrative coupling of different algorithms providing partly redundant information.

A flexible architecture for intelligent cruise control

We present a concept of a flexible and modular architecture for intelligent cruise control (ICC). The architecture can be subdivided into three different processing steps: object-related analysis of sensor data, behavior-based scene interpretation and behavior planning. Each step works on collected sensor information as well as on a knowledge base, which can be broadened by external knowledge like GPS and street maps. An intelligent car following system is described in the paper as a spin-off for behavior planning.

Image Processing for Driver Assistance

Systems for automated image analysis are useful for a variety of tasks and their importance is still growing due to technological advances and an increase of social acceptance. Especially in the field of driver assistance systems the progress in science has reached a level of high performance. Fully or partly autonomously guided vehicles, particularly for road-based traffic, pose high demands on the development of reliable algorithms due to the conditions imposed by natural environments. At the Institut fur Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We introduce a system which extracts the important information from an image taken by a CCD camera installed at the rear view mirror in a car. The approach consists of a sequential and a parallel sensor and information processing. Three main tasks namely the initial segmentation (object detection), the object tracking and the object classification are realized by integration in the sequential branch and by fusion in the parallel branch. The main gain of this approach is given by the integrative coupling of different algorithms providing partly redundant information.

Flexible architecture for driver assistance

To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems rose in the past years. Principal problems are caused by having a moving observer (ego motion) in predominantly natural surroundings. In this paper we present a solution for a flexible architecture for a driver assistance system. The architecture can be subdivided into four different parts: the object-related analysis, the knowledge base, the behavior-based scene interpretation, and the behavior planning unit. The object-related analysis is fed with data by the sensors (e.g., vision, radar). The sensor data are preprocessed (flexible sensor fusion) and evaluated (saliency map) searching for object-related information (positions, types of objects, etc.). The knowledge base is represented by static and dynamic knowledge. It consists of a set of rules (e.g. , traffic rules, physical laws), additional information (i.e., GPS, lane-information) and it is implicitly used by algorithms in the system. The scene interpretation combines the information extracted by the object related analysis and inspects the information for contradictions. It is strongly connected to the behavior planning using only information needed for the actual task. In the scene interpretation consistent representations (i.e., birds eye view) are organized and interpreted as well as a scene analysis is performed. The results of the scene interpretation are used for decision making in behavior planning, which is controlled by the actual task. The influence of behavior planning on the behavior of the guided vehicle is limited to advices as no mechanical control (e.g. , control of the steering angle) was implemented. An Intelligent Cruise Control (ICC) is shown as a spin-off for using this architecture.

Eine flexible Architektur für Fahrerassistenzsysteme

In diesem Artikel wird eine flexible Architektur vorgestellt, mit deren Hilfe eine modulare Losung von Fahrerassistenzaufgaben in Kraftfahrzeugen gezeigt werden kann. Es wird eine Objektbezogene Analyse von Sensordaten, eine Verhaltensbasierte Szeneninterpretation und eine Verhaltensplanung vorgestellt. Eine globale Wissensbasis, auf der jedes einzelne Modul arbeitet, beinhaltet die Beschreibung physikalischer Zusammenhange, Verhaltensregeln fur den Strasenverkehr, sowie Objekt- und Szenenwissen. Externes Wissen (z.B. GPS - Global Positioning System) kann ebenfalls in die Wissensbasis eingebunden werden. Als Anwendungsbeispiel der Verhaltensplanung wird ein intelligenter Tempomat vorgestellt.

A Flexible Architecture for Driver Assistance Systems

The problems encountered in building a driver assistance system are numerous. The collection of information about real environment by sensors is erroneous and incomplete. When the sensors are mounted on a moving observer it is difficult to find out whether a detected motion was caused by ego-motion or by an independent object moving. The collected data can be analyzed by several algorithms with different features designed for different tasks. To gain the demanded information their results have to be integrated and interpreted. In order to achieve an increase in reliability of information a stabilization over time and knowledge about important features have to be applied. Different solutions for driver assistance systems have been published. An approach proposed by Rossi et al. [8] showed an application for a security system. An application being tested on highways has been presented by Bertozzi and Broggi [1]. Dickmanns et al. presented a driving assistance system based on a 4D-approach [2]. Those systems were mainly designed for highway scenarios, while the architecture presented by Franke and Gorzig [3] has been tested in urban environment.