  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.
