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Multiple object tracking (MOT) is a crucial task in computer vision society.However, most tracking-by-detection MOT methods, with available detectedbounding boxes, cannot effectively handle static, slow-moving and fast-movingcamera scenarios simultaneously due to ego-motion and frequent occlusion. Inthis work, we propose a novel tracking framework, called "instance-aware MOT"(IA-MOT), that can track multiple objects in either static or moving cameras byjointly considering the instance-level features and object motions. First,robust appearance features are extracted from a variant of Mask R-CNN detectorwith an additional embedding head, by sending the given detections as theregion proposals. Meanwhile, the spatial attention, which focuses on theforeground within the bounding boxes, is generated from the given instancemasks and applied to the extracted embedding features. In the tracking stage,object instance masks are aligned by feature similarity and motion consistencyusing the Hungarian association algorithm. Moreover, object re-identification(ReID) is incorporated to recover ID switches caused by long-term occlusion ormissing detection. Overall, when evaluated on the MOTS20 and KITTI-MOTSdataset, our proposed method won the first place in Track 3 of the BMTTChallenge in CVPR2020 workshops.

IA-MOT: Instance-Aware Multi-Object Tracking with Motion Consistency