  To overcome challenges in multiple object tracking task, recent algorithms
use interaction cues alongside motion and appearance features. These algorithms
use graph neural networks or transformers to extract interaction features that
lead to high computation costs. In this paper, a novel interaction cue based on
geometric features is presented aiming to detect occlusion and re-identify lost
targets with low computational cost. Moreover, in most algorithms, camera
motion is considered negligible, which is a strong assumption that is not
always true and leads to ID Switch or mismatching of targets. In this paper, a
method for measuring camera motion and removing its effect is presented that
efficiently reduces the camera motion effect on tracking. The proposed
algorithm is evaluated on MOT17 and MOT20 datasets and it achieves the
state-of-the-art performance of MOT17 and comparable results on MOT20. The code
is also publicly available.
