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Title
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Recovering hard-to-find object instances by sampling context-based object proposals
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Author
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Abstract
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| In this paper we focus on improving object detection performance in terms of recall. We propose a post detection stage during which we explore the image with the objective of recovering missed detections. This exploration is performed by sampling object proposals in the image. We analyse four different strategies to perform this sampling, giving special attention to strategies that exploit spatial relations between objects. In addition, we propose a novel method to discover higher-order relations between groups of objects. Experiments on the challenging KITH dataset show that our proposed relations-based proposal generation strategies can help improving recall at the cost of a relatively low amount of object proposals. (C) 2016 Elsevier Inc. All rights reserved. |
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Language
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English
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Source (journal)
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Computer vision and image understanding. - -
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Publication
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2016
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ISSN
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1077-3142
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DOI
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10.1016/J.CVIU.2016.08.007
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Volume/pages
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152
(2016)
, p. 118-130
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ISI
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000387630900010
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Full text (Publisher's DOI)
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