Thomas Michalke

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Currently available traffic sign recognition systems typically focus on a single class of traffic sign and therefore, the algorithms are optimized to find only this specific class. To this end, a number of approaches for real time capable classification of mostly circular signs exist. Nevertheless, to simultaneously recognize a number of classes a different(More)
Biologically motivated attention systems prefilter the visual environment for scene elements that pop out most or match the current system task best. However, the robustness of biological attention systems is difficult to achieve, given e.g., the high variability of scene content, changes in illumination, and scene dynamics. Most computational attention(More)
First vision-based approaches for detecting the drivable road area on unmarked streets were introduced in recent years. Although most of these visual feature-based approaches show sound results in scenarios of limited complexity, they seem to lack the necessary system-inherent flexibility to run in complex cluttered environments under changing lighting(More)
Research on computer vision systems for driver assistance resulted in a variety of approaches mainly performing reactive tasks like, e.g., lane keeping. However, for a full understanding of generic traffic situations, integrated and more flexible approaches are needed. We present a system inspired by the human visual system. Based on combining taskdependent(More)
State-of-the-art advanced driver assistance systems (ADAS) typically focus on single tasks and therefore, have functionalities with clearly defined application areas. Although said ADAS functions (e.g. lane departure warning) show good performance, they lack general usability, as e.g. different modes of operation for highways and country roads. This paper(More)
Newly emerging, highly complex Advanced Driver Assistance Systems (ADAS) fuse the output of various system modules (e.g., lane detection, object classification). Such knowledge fusion is realized in order to gain additional information of the environment allowing for complex system tasks as path planning, the active search for specific objects and(More)
Driver assistance functionalities on the market are getting more and more sophisticated, which will lead to integrated systems that fuse the data of multiple sensors (e.g., camera, Photonic Mixer Device, Radar) and internal system percepts (e.g., detected objects and their states, detected road). One important future challenge will be to find smart(More)
Research on computer vision systems for driver assistance resulted in a variety of isolated approaches mainly performing very specialized tasks like, e. g., lane keeping or traffic sign detection. However, for a full understanding of generic traffic situations, integrated and flexible approaches are needed. We here present a highly integrated vision(More)
The use of computer vision for assisting the driver dates back to first research projects in the 80’s, but only recently the progress in vision research and the increase in computational power have resulted in actual products. Although impressive from the robustness point of view, these systems are optimized for specific problems and at best perform(More)
In recent years, innovative passive safety concepts have been developed that have the potential to further decrease the number of victims of traffic accidents. Such complex passive safety systems (i.e. the Daimler PRE-SAFE Pulse or inflating metal structures in the vehicle doors) typically require lead times for activation/preparation that are longer than(More)