Multi-Object Tracking (MOT) has gained lots attention from researchers and achieved remarkable progress in recent years. However, recent studies on MOT tend to use different basic models (e.g, detector and embedding model), training data and training/inference tricks, which makes it difficult to construct a fair comparison between their progress. In this paper, we revisit the classic tracker DeepSORT, and upgrades it from aspects of detection, embedding, and association. The proposed tracker, named StrongSORT, achieves great improvements over DeepSORT, and can serve as a strong and fair baseline for other methods. We also present two lightweight and plugand-play algorithms to solve two "missing" problems in MOT. Firstly, to solve the missing association problem, some works associate short tracklets into complete trajectories with computationally expensive models. Instead, we propose the appearancefree link model (AFLink) to perform global association without appearance information, which achieves a better trade-off between speed and accuracy. Secondly, for the missing detection problem, we propose Gaussian-smoothed interpolation (GSI), which improves the linear interpolation algorithm with Gaussian process regression. Moreover, AFLink and GSI can be plugged into various trackers with a negligible extra computational cost (1.7 ms and 7.1 ms per image, respectively, on MOT17). By integrating StrongSORT with the two algorithms, the final tracker StrongSORT++ achieves SOTA results on multiple benchmarks, i.e., MOT17, MOT20, DanceTrack and KITTI. Codes are available at https://github.com/dyhBUPT/StrongSORT and https://github.com/open-mmlab/mmtracking.
