Vehicle detection with a monocular video sensor offers significant potential in a wide area of promising automotive applications. In this work the shadows in daylight scenes are used to detect vehicles in low-quality camera images and determine their positions and velocities in relation to the own vehicle. We propose a new algorithm that segments shadows in a top-down-view. In contrast to other works no specific thresholds or strong shadow edges are needed. Instead we binarize the image at multiple thresholds and analyze the results to generate vehicle position hypotheses. These hypotheses are verified with a RANSAC-based temporal verification approach. The accuracy of our method is evaluated using monocular real world sequences and ground truth data. Our algorithm works robustly under various lighting conditions in real-time at full video frame rate.

In this video we show our current prototype for robot assisted endoscopy. The system requires only few and simple instructions from the surgeon, in order to guide the endoscope in an intelligent, autonomous, and safe way: The surgeon tells what to do and the robot decides how to carry out the task by choosing the best manipulation primitive in every control cycle. Several sensors are integrated into the decision process: The endoscope camera, a stereo camera system, a force/torque sensor, a biomechanical model based on CT data and statistical knowledge, a position and velocity sensor for the manipulator, and interface devices like a foot switch. The executed manipulation primitive is handled by a hybrid controller allowing to switch the control mode (e.g. trajectory following or force control) for each degree of freedom of the task frame individually.