Multiple Object Tracking Robust to Occlusions through 3D Geometric Constraints

In this paper, we propose Ground IoU (Gr-IoU) to address the data association problem in multi-object tracking. When tracking objects detected by cameras, ID switches where different IDs are assigned to the same object in consecutive frames—frequently occur, especially when objects are in close proximity or overlapping. To address this issue, we intro-duce Gr-IoU, which incorporates constraints from the 3D structure of the scene. Gr-IoU transforms conventional bounding boxes from image space to the ground plane using vanishing point geometry. The IoU calculated with these transformed bounding boxes demonstrates higher sensitivity to the front-back relationships between objects, resulting in improved data association accuracy and reduced ID switches. We evaluated the performance of Gr-IoU using the MOT17 and MOT20 datasets, which include various tracking scenarios such as crowded scenes and sequences with frequent occlusions. Experimental results confirm that Gr-IoU outperforms conventional real-time methods that do not utilize appearance features.