Thomas Kalinke

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.

Entropie als Maß des lokalen Informationsgehalts in Bildern zur Realisierung einer Aufmerksamkeitssteuerung

Basierend auf der Informationstheorie, die C. Shannon [Sha48] einfuhrte, wird der lokale Informationsgehalt in Bildern geschatzt. Hierbei wird die Entropie als Mas der zu erwartenden Information eines Bildausschnitts herangezogen. Dieses lokale Bildentropiemas realisiert eine Aufmerksamkeitssteuerung, die ein Teilmodul der Anwendung „Autonomes Fuhren von Fahrzeugen” bildet.

Scene analysis and organization of behavior in driver assistance systems

To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems arose in the past years. Fully or partly autonomously guided vehicles, particularly for road traffic, pose high demands on the development of reliable algorithms. Principal problems are caused by having a moving observer in predominantly 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 present a solution for a driver assistance system. We concentrate on the aspects of video-based scene analysis and organization of behavior.

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.

A Neural Network for Symmetry-Based Object Detection and Tracking

A Discrete Neural Network (DNN) is presented determining a measure of similarity between two images. The network provides a correspondence map linking points of both images depending on the characteristics and arrangement of their neighborhood. The DNN is applied to the detection of bilateral symmetries in images taken from scenes of motor ways. A set of hypothetical symmetry axis is evaluated by similarity measurement between the left and right image halves, respectively.

Kullback-Leibler Distanz als Maß zur Erkennung nicht rigider Objekte

Basierend auf der Kullback-Leibler Divergenz [KL51] wird ein Mas abgeleitet, mit Hilfe dessen insbesondere nicht rigide Objekte wie Fusganger und Zweirader, die im Gegensatz zu Pkws und Lkws neben der Skalierungs- und Translationsinvarianz weitere rotatorische Freiheitsgrade haben, verfolgt werden.