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Object Detection with Grammar Models
Ross B Girshick · Pedro Felzenszwalb · David Mcallester
Compositional models provide an elegant formalism for representing the visual appearance of highly variable objects. While such models are appealing from a theoretical point of view, it has been difficult to demonstrate that they lead to performance advantages on challenging datasets. Here we develop a grammar model for person detection and show that it outperforms previous high-performance systems on the PASCAL benchmark. Our model represents people using a hierarchy of deformable parts, variable structure and an explicit model of occlusion for partially visible objects. To train the model, we introduce a new discriminative framework for learning structured prediction models from weakly-labeled data.
Author Information
Ross B Girshick (UC Berkeley)
Pedro Felzenszwalb (Brown University)
David Mcallester (Toyota Tech Institute Chicago)
Related Events (a corresponding poster, oral, or spotlight)
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2011 Poster: Object Detection with Grammar Models »
Wed. Dec 14th 04:45 -- 10:59 PM Room None
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