Comparative study of multi-person tracking methods

Abstract

This paper presents a study of two tracking algorithms (SORT~\cite{7533003} and Tracktor++~\cite{2019}) that were ranked first positions on the MOT Challenge leaderboard (The MOTChallenge web page: https://motchallenge.net ). The purpose of this study is to discover the techniques used and to provide useful insights about these algorithms in the tracking pipeline that could improve the performance of MOT tracking algorithms. To this end, we adopted the popular tracking-by-detection approach. We trained our own Pedestrian Detection model using the MOT17Det dataset (MOT17Det : https://motchallenge.net/data/MOT17Det/ ). We also used a re-identification model trained on MOT17 dataset (MOT17 : https://motchallenge.net/data/MOT17/ ) for Tracktor++ to reduce the false re-identification alarms. We then present experimental results which shows that Tracktor++ is a better multi-person tracking algorithm than SORT. We also performed ablation studies to discover the contribution of re-identification(RE-ID) network and motion to the results of Tracktor++. We finally conclude by providing some recommendations for future research.