  We present a novel self quality evaluation metric SQE for parameters
optimization in the challenging yet critical multi-object tracking task.
Current evaluation metrics all require annotated ground truth, thus will fail
in the test environment and realistic circumstances prohibiting further
optimization after training. By contrast, our metric reflects the internal
characteristics of trajectory hypotheses and measures tracking performance
without ground truth. We demonstrate that trajectories with different qualities
exhibit different single or multiple peaks over feature distance distribution,
inspiring us to design a simple yet effective method to assess the quality of
trajectories using a two-class Gaussian mixture model. Experiments mainly on
MOT16 Challenge data sets verify the effectiveness of our method in both
correlating with existing metrics and enabling parameters self-optimization to
achieve better performance. We believe that our conclusions and method are
inspiring for future multi-object tracking in practice.
