OccludedPASCAL3D+
Introduced by Wang et al. in Robust Object Detection under Occlusion with Context-Aware CompositionalNets
The OccludedPASCAL3D+ is a dataset is designed to evaluate the robustness to occlusion for a number of computer vision tasks, such as object detection, keypoint detection and pose estimation. In the OccludedPASCAL3D+ dataset, we simulate partial occlusion by superimposing objects cropped from the MS-COCO dataset on top of objects from the PASCAL3D+ dataset. We only use ImageNet subset in PASCAL3D+, which has 10812 testing images.
Source: OccludedPASCAL3D+Papers
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