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Occlusion handling is one of the challenges of object detection andsegmentation, and scene understanding. Because objects appear differently whenthey are occluded in varying degree, angle, and locations. Therefore,determining the existence of occlusion between objects and their order in ascene is a fundamental requirement for semantic understanding. Existing worksmostly use deep learning based models to retrieve the order of the instances inan image or for occlusion detection. This requires labelled occluded data andit is time consuming. In this paper, we propose a simpler and faster methodthat can perform both operations without any training and only requires themodal segmentation masks. For occlusion detection, instead of scanning the twoobjects entirely, we only focus on the intersected area between their boundingboxes. Similarly, we use the segmentation mask inside the same area to recoverthe depth-ordering. When tested on COCOA dataset, our method achieves +8% and+5% more accuracy than the baselines in order recovery and occlusion detectionrespectively.

BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery