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

EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking