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Counting in the Wild

  • Carlos ArtetaEmail author
  • Victor Lempitsky
  • Andrew Zisserman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9911)

Abstract

In this paper we explore the scenario of learning to count multiple instances of objects from images that have been dot-annotated through crowdsourcing. Specifically, we work with a large and challenging image dataset of penguins in the wild, for which tens of thousands of volunteer annotators have placed dots on instances of penguins in tens of thousands of images. The dataset, introduced and released with this paper, shows such a high-degree of object occlusion and scale variation that individual object detection or simple counting-density estimation is not able to estimate the bird counts reliably.

To address the challenging counting task, we augment and interleave density estimation with foreground-background segmentation and explicit local uncertainty estimation. The three tasks are solved jointly by a new deep multi-task architecture. Using this multi-task learning, we show that the spread between the annotators can provide hints about local object scale and aid the foreground-background segmentation, which can then be used to set a better target density for learning density prediction. Considerable improvements in counting accuracy over a single-task density estimation approach are observed in our experiments.

Keywords

Depth Information Convolution Neural Network Counting Task Object Count Segmentation Mask 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Dr. Tom Hart and the Zooniverse team for their leading role in the penguin watch project. Financial support was provided by the RCUK Centre for Doctoral Training in Healthcare Innovation (EP/G036861/1) and the EPSRC Programme Grant Seebibyte EP/M013774/1.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Carlos Arteta
    • 1
    Email author
  • Victor Lempitsky
    • 2
  • Andrew Zisserman
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Skolkovo Institute of Science and Technology (Skoltech)MoscowRussia

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