  Multi-object tracking (MOT) aims at estimating bounding boxes and identities
of objects in videos. Most methods obtain identities by associating detection
boxes whose scores are higher than a threshold. The objects with low detection
scores, e.g. occluded objects, are simply thrown away, which brings
non-negligible true object missing and fragmented trajectories. To solve this
problem, we present a simple, effective and generic association method,
tracking by associating almost every detection box instead of only the high
score ones. For the low score detection boxes, we utilize their similarities
with tracklets to recover true objects and filter out the background
detections. When applied to 9 different state-of-the-art trackers, our method
achieves consistent improvement on IDF1 score ranging from 1 to 10 points. To
put forwards the state-of-the-art performance of MOT, we design a simple and
strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3
IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a
single V100 GPU. ByteTrack also achieves state-of-the-art performance on MOT20,
HiEve and BDD100K tracking benchmarks. The source code, pre-trained models with
deploy versions and tutorials of applying to other trackers are released at
https://github.com/ifzhang/ByteTrack.
