Vision-based driver assistance systems have great potential for preventing fatalities. This work addresses the problem of 3D monocular vehicle tracking and turn rate estimation in situations where vehicles need to be tracked along intersections and curves. To estimate the tracked vehicle's turn rate, an approach based on image feature correspondences and a simplified geometric vehicle model is used. The model is robustly and efficiently fitted to the matched image features using an improved RANSAC scheme that automatically enforces physically plausible vehicle motions and speeds up the overall system at the same time. Temporal integration of the computed turn rates is performed by an Extended Kalman Filter with the bicycle motion model. Experiments with real world data show the applicability and robustness of the proposed concepts.

This paper introduces simultaneous globally optimal hand-eye self-calibration in both its rotational and translational components. The main contributions are new feasibility tests to integrate the hand-eye calibration problem into a branch-and-bound parameter space search. The presented method constitutes the first guaranteed globally optimal estimator for simultaneous optimization of both components with respect to a cost function based on reprojection errors. The system is evaluated in both synthetic and real world scenarios. The employed benchmark dataset is published online1to create a common point of reference for evaluation of hand-eye self-calibration algorithms.

This paper introduces simultaneous global optimization of both camera orientation and vehicle wheel circumference without requiring any information about the translations in the system. The main contribution are new objective function bounds to integrate this problem into a branch-and-bound parameter space search. The presented method constitutes the first guaranteed globally optimal estimator for both components of the problem with respect to a cost function based on reprojection errors. The algorithm operates directly on image measurements and does not depend on any structure and motion preprocessing to estimate camera poses. The complete system is implemented and validated on both synthetic and real automotive datasets.