Simple Cues Lead to a Strong Multi-Object Tracker

For a long time, the most common paradigm in Multi-Object Tracking was tracking-by-detection (TbD), where objects are first detected and then associated over video frames. For association, most models resource to motion and appearance cues. While still relying on these cues, recent approaches based on, e.g., attention have shown an ever-increasing need for training data and overall complex frameworks. We claim that 1) strong cues can be obtained from little amounts of training data if some key design choices are applied, 2) given these strong cues, standard Hungarian matching-based association is enough to obtain impressive results. Our main insight is to identify key components that allow a standard reidentification network to excel at appearance-based tracking. We extensively analyze its failure cases and show that a combination of our appearance features with a simple motion model leads to strong tracking results. Our model achieves state-of-the-art performance on MOT17 and MOT20 datasets outperforming previous state-of-the-art trackers by up to 5.4pp in IDF1 and 4.4pp in HOTA. We will release the code and models after the paper's acceptance.