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Asian Conference on Computer Vision

ACCV 2018: Computer Vision – ACCV 2018 pp 480–495Cite as

Contextual Object Detection with a Few Relevant Neighbors

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11362))

Abstract

A natural way to improve the detection of objects is to consider the contextual constraints imposed by the detection of additional objects in a given scene. In this work, we exploit the spatial relations between objects in order to improve detection capacity, as well as analyze various properties of the contextual object detection problem. To precisely calculate context-based probabilities of objects, we developed a model that examines the interactions between objects in an exact probabilistic setting, in contrast to previous methods that typically utilize approximations based on pairwise interactions. Such a scheme is facilitated by the realistic assumption that the existence of an object in any given location is influenced by only few informative locations in space. Based on this assumption, we suggest a method for identifying these relevant locations and integrating them into a mostly exact calculation of probability based on their raw detector responses. This scheme is shown to improve detection results and provides unique insights about the process of contextual inference for object detection. We show that it is generally difficult to learn that a particular object reduces the probability of another, and that in cases when the context and detector strongly disagree this learning becomes virtually impossible for the purposes of improving the results of an object detector. Finally, we demonstrate improved detection results through use of our approach as applied to the PASCAL VOC and COCO datasets.

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Acknowledgments

This research was supported in part by Israel Ministry of Science, Technology and Space (MOST Grant 54178). We also thank the Frankel Fund and the Helmsley Charitable Trust through the ABC Robotics Initiative, both at Ben-Gurion University of the Negev, for their generous support.

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Authors and Affiliations

  1. Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel

    Ehud Barnea & Ohad Ben-Shahar

Authors
  1. Ehud Barnea
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  2. Ohad Ben-Shahar
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Corresponding author

Correspondence to Ehud Barnea .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C. V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Barnea, E., Ben-Shahar, O. (2019). Contextual Object Detection with a Few Relevant Neighbors. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11362. Springer, Cham. https://doi.org/10.1007/978-3-030-20890-5_31

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  • DOI: https://doi.org/10.1007/978-3-030-20890-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20889-9

  • Online ISBN: 978-3-030-20890-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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