  Multi-object tracking (MOT) enables mobile robots to perform well-informed
motion planning and navigation by localizing surrounding objects in 3D space
and time. Existing methods rely on depth sensors (e.g., LiDAR) to detect and
track targets in 3D space, but only up to a limited sensing range due to the
sparsity of the signal. On the other hand, cameras provide a dense and rich
visual signal that helps to localize even distant objects, but only in the
image domain. In this paper, we propose EagerMOT, a simple tracking formulation
that eagerly integrates all available object observations from both sensor
modalities to obtain a well-informed interpretation of the scene dynamics.
Using images, we can identify distant incoming objects, while depth estimates
allow for precise trajectory localization as soon as objects are within the
depth-sensing range. With EagerMOT, we achieve state-of-the-art results across
several MOT tasks on the KITTI and NuScenes datasets. Our code is available at
https://github.com/aleksandrkim61/EagerMOT.
