  In this paper, we propose a highly practical fully online multi-object
tracking and segmentation (MOTS) method that uses instance segmentation results
as an input. The proposed method is based on the Gaussian mixture probability
hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a
mask-based affinity fusion (MAF) model to achieve high-performance online
tracking. The HDA consists of two associations: segment-to-track and
track-to-track associations. One affinity, for position and motion, is computed
by using the GMPHD filter, and the other affinity, for appearance is computed
by using the responses from a single object tracker such as a kernalized
correlation filter. These two affinities are simply fused by using a
score-level fusion method such as min-max normalization referred to as MAF. In
addition, to reduce the number of false positive segments, we adopt mask
IoU-based merging (mask merging). The proposed MOTS framework with the key
modules: HDA, MAF, and mask merging, is easily extensible to simultaneously
track multiple types of objects with CPU only execution in parallel processing.
In addition, the developed framework only requires simple parameter tuning
unlike many existing MOTS methods that need intensive hyperparameter
optimization. In the experiments on the two popular MOTS datasets, the key
modules show some improvements. For instance, ID-switch decreases by more than
half compared to a baseline method in the training sets. In conclusion, our
tracker achieves state-of-the-art MOTS performance in the test sets.
