论文标题
BBBD：基于边界盒的检测器，用于遮挡检测和订单恢复
BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery
论文作者
论文摘要
遮挡处理是对象检测和细分以及场景理解的挑战之一。因为当物体以不同的程度，角度和位置遮挡时，它们会出现不同。因此，确定对象与它们在场景中的顺序之间的遮挡是语义理解的基本要求。现有作品主要使用基于深度学习的模型来检索图像或遮挡检测中实例的顺序。这需要标记为封闭的数据，这很耗时。在本文中，我们提出了一种更简单，更快的方法，可以在没有任何培训的情况下执行这两个操作，只需要模态分割掩码。为了进行遮挡检测，我们只专注于它们的边界框之间的相交区域，而不是完全扫描两个对象。同样，我们使用同一区域内的分割掩码来恢复深度排序。当在可可数据集上进行测试时，我们的方法可达到 +8％和 +5％的准确性，分别以恢复和遮挡检测分别提高基准。
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.
