Thomas Zielke

Vision-based car-following: detection, tracking, and identification

The authors have developed a vision system for automatic car following and object classification. The CARTRACK system can reliably detect, track, and identify the back of an automobile in a dynamic image taken from a following car in the same lane or in a neighbor lane. The detection and tracking system exploits the symmetry property of the rear view of normal cars. The class of objects that are detected by CARTRACK includes normal cars of all sizes as well as trucks and conventional trailers. Parallel to the tracking process, a model-based identification module identifies the type of vehicle being followed. For objects of known size, it also facilitates distance estimation. Deformable 2D models (planar feature graphs) constructed from various visual features are used. The image features in a region of interest selected by the symmetry-based detection process are matched with the model objects by means of an elastic net technique.<<ETX>>

Intensity and Edge-Based Symmetry Detection Applied to Car-Following

We present two methods for detecting symmetry in images, one based directly on the intensity values and another one based on a discrete representation of local orientation. A symmetry finder has been developed which uses the intensity-based method to search an image for compact regions which display some degree of mirror symmetry due to intensity similarities across a straight axis. In a different approach, we look at symmetry as a bilateral relationship between local orientations. A symmetryenhancing edge detector is presented which indicates edges dependent on the orientations at two different image positions. SEED, as we call it, is a detector element implemented by a feedforward network that holds the symmetry conditions. We use SEED to find the contours of symmetric objects of which we know the axis of symmetry from the intensity-based symmetry finder. The methods presented have been applied to the problem of visually guided car-following. Real-time experiments with a system for automatic headway control on motorways have been successful.

Visual obstacle detection for automatically guided vehicles

A stereo obstacle detection system has been developed for automatically guided vehicles that operate on flat (factory) floors. The system does not attempt to reconstruct the 3D environment visually but simply tries to detect obstacles on the floor in the vehicles path. The approach to stereo image processing uses inverse perspective mappings to facilitate matching of the binocular field of vision against the expected 3D structure of the environment. Assuming a known relative camera model, a geometrical image transformation is computed which essentially compensates the stereo disparities for the image points of the floor. After the mapping operation the images are compared and local mismatches are interpreted as possible obstacle locations. The system has been successfully tested in a factory environment. The implementation runs on standard microprocessor hardware in real time.<<ETX>>

CARTRACK: computer vision-based car following

CARTRACK is a computer vision system that can reliably detect, track, and measure vehicle rears in images from a video camera in a following car. The system exploits the symmetry property typical for the rear of most vehicles on normal roads. The authors present two novel methods for detecting mirror symmetry in images, one based directly on the intensity values and another one based on a discrete representation of local orientation. CARTRACK has been used for realtime experiments with test vehicles of Volkswagen and Daimler-Benz.<<ETX>>

An integrated obstacle detection framework for intelligent cruise control on motorways

This paper deals with the development and implementation of a purely visual obstacle detection framework for autonomous driving on motorways. Our activities are embedded in the SMART VEHICLE subproject of the ESPRIT project CLEOPATRA. The aim of SMART VEHICLE is the development of a visually controlled intelligent cruise control (ICC) for a prototype passenger car, the Mercedes-Benz research car VITA II. The vision modules are operating concurrently on a net of digital signal processors with multiple video inputs. Our obstacle detection framework bases on the application of highly adapted machine-vision elements such as robust symmetry measuring, neural net-based adaptive object recognition, real-time tracking of multiple vehicles, and inverse-perspective stereo image matching (IPM). We will show detailed results from extensive closed-loop autonomous driving on public motorways and we will present the final HPC hardware system which is part of the application computer of VITA II.

Integrating Face Recognition into Security Systems

Automated processing of facial images has become a serious market for both hard- and software products. For the commercial success of face recognition systems it is most crucial that the process of face image capturing is very convenient for the people exposed to such systems. As a consequence, the whole imaging setup has to be carefully designed for each operational scenario.

Matching conic curve segments

For image contours approximated by conic curve segments. The authors examine how to detect efficiently, given geometric relationships between conic segments. They present a method that can serve as a general tool for model-driven matching of conic curve segments.<<ETX>>