  Driven by recent advances in object detection with deep neural networks, the
tracking-by-detection paradigm has gained increasing prevalence in the research
community of multi-object tracking (MOT). It has long been known that
appearance information plays an essential role in the detection-to-track
association, which lies at the core of the tracking-by-detection paradigm.
While most existing works consider the appearance distances between the
detections and the tracks, they ignore the statistical information implied by
the historical appearance distance records in the tracks, which can be
particularly useful when a detection has similar distances with two or more
tracks. In this work, we propose a hybrid track association (HTA) algorithm
that models the historical appearance distances of a track with an incremental
Gaussian mixture model (IGMM) and incorporates the derived statistical
information into the calculation of the detection-to-track association cost.
Experimental results on three MOT benchmarks confirm that HTA effectively
improves the target identification performance with a small compromise to the
tracking speed. Additionally, compared to many state-of-the-art trackers, the
DeepSORT tracker equipped with HTA achieves better or comparable performance in
terms of the balance of tracking quality and speed.
