BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery
Kaziwa Saleh, Zoltan Vamossy

TL;DR
This paper introduces a simple, training-free method for occlusion detection and order recovery using bounding box intersections and segmentation masks, outperforming existing deep learning approaches on the COCOA dataset.
Contribution
The proposed approach eliminates the need for labeled occluded data and training by leveraging bounding box intersections and segmentation masks for occlusion and order detection.
Findings
Achieves +8% accuracy in order recovery
Achieves +5% accuracy in occlusion detection
Operates without training or labeled occlusion data
Abstract
Occlusion handling is one of the challenges of object detection and segmentation, and scene understanding. Because objects appear differently when they are occluded in varying degree, angle, and locations. Therefore, determining the existence of occlusion between objects and their order in a scene is a fundamental requirement for semantic understanding. Existing works mostly use deep learning based models to retrieve the order of the instances in an image or for occlusion detection. This requires labelled occluded data and it is time consuming. In this paper, we propose a simpler and faster method that can perform both operations without any training and only requires the modal segmentation masks. For occlusion detection, instead of scanning the two objects entirely, we only focus on the intersected area between their bounding boxes. Similarly, we use the segmentation mask inside the…
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