  Multi-Object Tracking (MOT) aims to maintain stable and uninterrupted
trajectories for each target. Most state-of-the-art approaches first detect
objects in each frame and then implement data association between new
detections and existing tracks using motion models and appearance similarities.
Despite achieving satisfactory results, occlusion and crowds can easily lead to
missing and distorted detections, followed by missing and false associations.
In this paper, we first revisit the classic tracker DeepSORT, enhancing its
robustness over crowds and occlusion significantly by placing greater trust in
predictions when detections are unavailable or of low quality in crowded and
occluded scenes. Specifically, we propose a new framework comprising of three
lightweight and plug-and-play algorithms: the probability map, the prediction
map, and the covariance adaptive Kalman filter. The probability map identifies
whether undetected objects have genuinely disappeared from view (e.g., out of
the image or entered a building) or are only temporarily undetected due to
occlusion or other reasons. Trajectories of undetected targets that are still
within the probability map are extended by state estimations directly. The
prediction map determines whether an object is in a crowd, and we prioritize
state estimations over observations when severe deformation of observations
occurs, accomplished through the covariance adaptive Kalman filter. The
proposed method, named MapTrack, achieves state-of-the-art results on popular
multi-object tracking benchmarks such as MOT17 and MOT20. Despite its superior
performance, our method remains simple, online, and real-time. The code will be
open-sourced later.
