  Multi-object tracking (MOT) is a prominent task in computer vision with
application in autonomous driving, responsible for the simultaneous tracking of
multiple object trajectories. Detection-based multi-object tracking (DBT)
algorithms detect objects using an independent object detector and predict the
imminent location of each target. Conventional prediction methods in DBT
utilize Kalman Filter(KF) to extrapolate the target location in the upcoming
frames by supposing a constant velocity motion model. These methods are
especially hindered in autonomous driving applications due to dramatic camera
motion or unavailable detections. Such limitations lead to tracking failures
manifested by numerous identity switches and disrupted trajectories. In this
paper, we introduce a novel KF-based prediction module called the Ego-motion
Aware Target Prediction (EMAP) module by focusing on the integration of camera
motion and depth information with object motion models. Our proposed method
decouples the impact of camera rotational and translational velocity from the
object trajectories by reformulating the Kalman Filter. This reformulation
enables us to reject the disturbances caused by camera motion and maximizes the
reliability of the object motion model. We integrate our module with four
state-of-the-art base MOT algorithms, namely OC-SORT, Deep OC-SORT, ByteTrack,
and BoT-SORT. In particular, our evaluation on the KITTI MOT dataset
demonstrates that EMAP remarkably drops the number of identity switches (IDSW)
of OC-SORT and Deep OC-SORT by 73% and 21%, respectively. At the same time, it
elevates other performance metrics such as HOTA by more than 5%. Our source
code is available at https://github.com/noyzzz/EMAP.
