4 accepted papers
Tracking in urban street scenes plays a central role in autonomous systems such as self-driving cars. Most of the current vision-based tracking methods perform tracking in the image domain. Other approaches, e.g. based on LIDAR and radar, track purely in 3D. While some vision-based tracking methods…
Semantic image segmentation is an essential component of modern autonomous driving systems, as an accurate understanding of the surrounding scene is crucial to navigation and action planning. Current state-of-the-art approaches in semantic image segmentation rely on pre-trained networks that were in…
Abstract In this paper, we address the problem of object discovery in time-varying, large-scale image collections. A core part of our approach is a novel Limited Horizon Minimum Spanning Tree (LH-MST) structure that closely approximates the Minimum Spanning Tree at a small fraction of the latter's c…
Most vision based systems for object tracking in urban environments focus on a limited number of important object categories such as cars or pedestrians, for which powerful detectors are available. However, practical driving scenarios contain many additional objects of interest, for which suitable d