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

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 same area to recover the depth-ordering. When tested on COCOA dataset, our method achieves +8% and +5% more accuracy than the baselines in order recovery and occlusion detection respectively.