The KITTI Vision Benchmark Suite

Most monocular object tracking algorithms work
in the 2D domain of the image. However, object trajectories,
which are very simple in a fixed 3D world space, result in
complex motions on the image plane, especially when the camera
is moving. Therefore, in absence of any 3D representation, afore-
mentioned approaches are only able to perform the measurement-
to-track association based on rough similarity of 2D bounding box
parameters. Recent advances in monocular 3D object detection
allow to extract additional parameters like the pose and spatial
extent of a 3D bounding box. In this paper, we present a multi-
object tracking approach composed of an Extended Kalman
filter estimating the 3D state by using these detections for track
initialization. In subsequent time steps 2D bounding boxes are
used to avoid filtering temporally correlated 3D measurements.
This ensures properly estimated state uncertainties. (For more see paper)

Will be provided with Paper

@INPROCEEDINGS{9626850,

author={Reich, Andreas and Wuensche, Hans-Joachim},

booktitle={2021 IEEE 24th International Conference on Information Fusion (FUSION)},

title={Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks},

year={2021},

volume={},

number={},

pages={1-7},

doi={}}