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To overcome challenges in multiple-object tracking (MOT) tasks, 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 reidentify lost targets with low computational costs. Moreover, in the majority of algorithms, camera motion is considered negligible, which is a strong assumption that is not always true and can lead to identity (ID) switching or mismatching of targets. In this paper, a method for measuring camera motion is presented that efficiently reduces its effect on tracking. The proposed algorithm is evaluated on MOT17 and MOT20 datasets and achieves state-of-the-art performance on MOT17 with comparable results on MOT20. The code is also publicly available. 1 1 https://github.com/mhnasseri/for_tracking. • Cascade association based on the detection score. • A new similarity metric that considers detection's size. • A novel interaction model for occlusion handling and target re-identification. • A novel algorithm for estimating camera motion and removing it. • State-of-the-art results on MOT17 and comparable results on MOT20. [ABSTRACT FROM AUTHOR]