  In this paper, we propose an efficient online multi-object tracking framework
based on the GMPHD filter and occlusion group management scheme where the GMPHD
filter utilizes hierarchical data association to reduce the false negatives
caused by miss detection. The hierarchical data association consists of two
steps: detection-to-track and track-to-track associations, which can recover
the lost tracks and their switched IDs. In addition, the proposed framework is
equipped with an object grouping management scheme which handles occlusion
problems with two main parts. The first part is "track merging" which can merge
the false positive tracks caused by false positive detections from occlusions,
where the false positive tracks are usually occluded with a measure. The
measure is the occlusion ratio between visual objects,
sum-of-intersection-over-area (SIOA) we defined instead of the IOU metric. The
second part is "occlusion group energy minimization (OGEM)" which prevents the
occluded true positive tracks from false "track merging". We define each group
of the occluded objects as an energy function and find an optimal hypothesis
which makes the energy minimal. We evaluate the proposed tracker in benchmark
datasets such as MOT15 and MOT17 which are built for multi-person tracking. An
ablation study in training dataset shows that not only "track merging" and
"OGEM" complement each other but also the proposed tracking method has more
robust performance and less sensitive to parameters than baseline methods.
Also, SIOA works better than IOU for various sizes of false positives.
Experimental results show that the proposed tracker efficiently handles
occlusion situations and achieves competitive performance compared to the
state-of-the-art methods. Especially, our method shows the best multi-object
tracking accuracy among the online and real-time executable methods.
