Abstract—This study introduces an approach to threedimensional vehicle pose estimation using a stereo camera system. After computation of stereo and optical flow on the investigated scene, a four-dimensional clustering approach separates the static from the moving objects in the scene. The iterative closest point algorithm (ICP) estimates the vehicle pose using a cuboid as a weak vehicle model. In contrast to classical ICP optimisation a polar distance metric is used which especially takes into account the error distribution of the stereo measurement process. The tracking approach is based on tracking-by-detection such that no temporal filtering is used. The method is evaluated on seven different real-world sequences, where different stereo algorithms, baseline distances, distance metrics, and optimisation algorithms are examined. The results show that the proposed polar distance metric yields a higher accuracy for yaw angle estimation of vehicles than the common Euclidean distance metric, especially when using pixel-accurate stereo points. I.

In this contribution we introduce the Shape Flow algorithm (SF), a novel method for spatio-temporal 3D pose estimation of a 3D parametric curve. The SF is integrated into a tracking system and its suitability for tracking human body parts in 3D is examined. Based on the example of tracking the human hand-forearm limb it is shown that the use of two SF instances with different initialisations leads to an accurate and temporally stable tracking system. In our framework, the temporal pose derivative is available instantaneously, therefore we avoid delays typically encountered when filtering the pose estimation results over time. All necessary information is obtained from the images, only a coarse initialisation of the model parameters is required. Experimental investigations are performed on 5 real-world test sequences showing 3 different test persons in an average distance of 1.2–3.3 m to the camera in front of cluttered background. We achieve typical pose estimation accuracies of 40–100 mm for the mean distance to the ground truth and 4–6 mm for the pose differences between subsequent images. 1

This study regards the problem of incorrect stereo matches due to the occurrence of repetitive structures in the scene. In stereo vision, repetitive structures may lead to “phantom objects ” in front of or behind the true scene which cause severe problems in scenarios involving mobile robot navigation or human-robot interaction. To alleviate this problem, we propose a model-based method which is independent of the specific stereo algorithm used. The basic idea is the feedback of application dependent model information into the correspondence analysis procedure without loosing the ability to reconstruct scene parts not described by the model. The employed scene models may either consist of a single plane or (for modelling more complex objects) of several connected planes. An FFT-based detection stage allows the extraction of scene parts displaying repetitive structures and yields the orientation of the model plane, while the plane distance is inferred with a robust optimisation technique based on a model-free stereo analysis. Alternatively, motion-based segmentation can be applied. Our experimental evaluation performed on manually