Occlusion Reasoning for Object Detection under Arbitrary Viewpoint

IEEE Transactions on Pattern Analysis and Machine Intelligence
Edward Hsiao, Martial Hebert

Abstract

We present a unified occlusion model for object instance detection under arbitrary viewpoint. Whereas previous approaches primarily modeled local coherency of occlusions or attempted to learn the structure of occlusions from data, we propose to explicitly model occlusions by reasoning about 3D interactions of objects. Our approach accurately represents occlusions under arbitrary viewpoint without requiring additional training data, which can often be difficult to obtain. We validate our model by incorporating occlusion reasoning with the state-of-the-art LINE2D and Gradient Network methods for object instance detection and demonstrate significant improvement in recognizing texture-less objects under severe occlusions.

References

Nov 15, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·V FerrariC Schmid
Aug 3, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Piotr DollárPietro Perona
Mar 24, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Stefan HinterstoisserVincent Lepetit

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Citations

May 15, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Homa Foroughi Hong Zhang

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