Multiobject Tracking and Segmentation With Embedding Mask-Based Affinity Fusion in Hierarchical Data Association

In this paper, we propose a highly feasible 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 single object trackers such as kernalized correlation filter, SiamRPN, and DaSiamRPN. 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.

Automated summary

In this paper, we propose a highly feasible fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method achieves high-performance online tracking by using the GMPHD filter, HDA, and MAF model. Our tracker achieves state-of-the-art MOTS performance in the experiments on two popular MOTS datasets.