Stephanie Lessmann

TL;DR: A probabilistic solution that integrates distance estimation in a vehicle tracking environment by using a ground plane angle based estimation together with a width interval constraint utilizing a vehicle classifier and a Bayes estimator is described.

TL;DR: In this paper, a method of determining the distance of an object from an automated vehicle based on images taken by a monocular image acquiring device is presented, where the object is recognized with an object-class by means of an image processing system.

TL;DR: This paper presents a method to generate a meaningful confidence measurement during online real-time egomotion estimation of a vehicle using a monocular camera and shows that this confidence measurement gives reliable results and can be used to filter the egomotions estimation using a Kalman filter.

TL;DR: A novel approach which is fast to compute and robust, utilize a depth prior for the translation and integrate robust estimation techniques, like MSAC and an M-estimator, and can be computed in real time.

TL;DR: In this paper, a method for determining the distance of an object from a motor vehicle comprising the steps of using a monocular image capturing device at intervals images of the vehicle environment are included, recognized at least one object of interest by means of an image processing system in the captured images and an object class is assigned, taken from the images are obtained using a pinhole camera model based on the object class respective position information showing the position of a reference point of the detected object relative to the road plane in world coordinates specify a scaling factor of the model using a Bayesian estimator