  Multi-Object Tracking (MOT) is a crucial computer vision task that aims to
predict the bounding boxes and identities of objects simultaneously. While
state-of-the-art methods have made remarkable progress by jointly optimizing
the multi-task problems of detection and Re-ID feature learning, yet, few
approaches explore to tackle the occlusion issue, which is a long-standing
challenge in the MOT field. Generally, occluded objects may hinder the detector
from estimating the bounding boxes, resulting in fragmented trajectories. And
the learned occluded Re-ID embeddings are less distinct since they contain
interferer. To this end, we propose an occlusion-aware detection and Re-ID
calibrated network for multi-object tracking, termed as ORCTrack. Specifically,
we propose an Occlusion-Aware Attention (OAA) module in the detector that
highlights the object features while suppressing the occluded background
regions. OAA can serve as a modulator that enhances the detector for some
potentially occluded objects. Furthermore, we design a Re-ID embedding matching
block based on the optimal transport problem, which focuses on enhancing and
calibrating the Re-ID representations through different adjacent frames
complementarily. To validate the effectiveness of the proposed method,
extensive experiments are conducted on two challenging VisDrone2021-MOT and
KITTI benchmarks. Experimental evaluations demonstrate the superiority of our
approach, which can achieve new state-of-the-art performance and enjoy high
run-time efficiency.
