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Occlusion Patterns for Object Class Detection

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by Bojan Pepik , Michael Stark , Peter Gehler Bernt Schiele
Citations:13 - 1 self
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BibTeX

@MISC{Pepik_occlusionpatterns,
    author = {Bojan Pepik and Michael Stark and Peter Gehler Bernt Schiele},
    title = {Occlusion Patterns for Object Class Detection},
    year = {}
}

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Abstract

Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion re-mains a major challenge, and a principled solution is yet to be found. In this paper we leave the beaten path of meth-ods that treat occlusion as just another source of noise – instead, we include the occluder itself into the modelling, by mining distinctive, reoccurring occlusion patterns from annotated training data. These patterns are then used as training data for dedicated detectors of varying sophistica-tion. In particular, we evaluate and compare models that range from standard object class detectors to hierarchical, part-based representations of occluder/occludee pairs. In an extensive evaluation we derive insights that can aid fur-ther developments in tackling the occlusion challenge. 1.

Keyphrases

occlusion pattern    object class detection    standard object class detector    training data    principled solution    annotated training data    occluder occludee pair    occlusion challenge    dedicated detector    long-standing problem    partial occlusion    fur-ther development    beaten path    extensive evaluation    recent object class recognition system    part-based representation    major challenge   

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