Occlusion Pattern Discovery for Object Detection and Occlusion Reasoning

Chunluan Zhou, Junsong Yuan

Abstract: Despite recent progress of object category detection in real scenes, detecting objects that are partially or heavily occluded remains a challenging problem due to the uncertainty and diversity of occlusion situations which could cause large intra-category appearance variance. To learn these occlusion situations, we propose a novel approach to discover occlusion patterns that cannot only boost occluded object detection but also provide occlusion reasoning. Our approach is based on a classic deformable part model (DPM) trained on fully observed object examples. Each occlusion pattern contains only a subset of visible parts, thus the total number of occlusion patterns are exponential to the number of parts, i.e., m parts will generate 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> occlusion patterns to compose an occlusion pattern pool. From this occlusion pattern pool, we look for a small group of occlusion patterns that are: (1) representative patterns that can well explain training examples and (2) discriminative patterns that have high detection performance individually. To select such occlusion patterns, we formulate occlusion pattern discovery as a facility location problem, which can be solved effectively by greedy search. The discovered occlusion patterns are themselves DPMs and can be used as object detectors when properly tuned. They can also be combined with the state-of-the-art detectors (e.g. Faster R-CNN) for improving detection performance and achieving part-level occlusion reasoning. The effectiveness of the proposed approach is validated on Pascal VOC2007 and VOC2010 datasets.