  Multiple Object Tracking (MOT) has rapidly progressed in recent years.
Existing works tend to design a single tracking algorithm to perform both
detection and association. Though ensemble learning has been exploited in many
tasks, i.e, classification and object detection, it hasn't been studied in the
MOT task, which is mainly caused by its complexity and evaluation metrics. In
this paper, we propose a simple but effective ensemble method for MOT, called
EnsembleMOT, which merges multiple tracking results from various trackers with
spatio-temporal constraints. Meanwhile, several post-processing procedures are
applied to filter out abnormal results. Our method is model-independent and
doesn't need the learning procedure. What's more, it can easily work in
conjunction with other algorithms, e.g., tracklets interpolation. Experiments
on the MOT17 dataset demonstrate the effectiveness of the proposed method.
Codes are available at https://github.com/dyhBUPT/EnsembleMOT.
