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Tracking in urban street scenes plays a central role in autonomous systemssuch as self-driving cars. Most of the current vision-based tracking methodsperform tracking in the image domain. Other approaches, eg based on LIDAR andradar, track purely in 3D. While some vision-based tracking methods invoke 3Dinformation in parts of their pipeline, and some 3D-based methods utilizeimage-based information in components of their approach, we propose to useimage- and world-space information jointly throughout our method. We presentour tracking pipeline as a 3D extension of image-based tracking. From enhancingthe detections with 3D measurements to the reported positions of every trackedobject, we use world-space 3D information at every stage of processing. Weaccomplish this by our novel coupled 2D-3D Kalman filter, combined with aconceptually clean and extendable hypothesize-and-select framework. Ourapproach matches the current state-of-the-art on the official KITTI benchmark,which performs evaluation in the 2D image domain only. Further experiments showsignificant improvements in 3D localization precision by enabling our coupled2D-3D tracking.

Combined Image- and World-Space Tracking in Traffic Scenes