Specialized Cyclist Detection Dataset: Challenging Real-World Computer Vision Dataset for Cyclist Detection Using a Monocular RGB Camera

Alexander Masalov1, Pavel Matrenin1, Jeffrey Ota2, Florian Wirth3, Christoph Stiller4, Heath Edwin Corbet5, Eric Lee6

  • 1WINKAM
  • 2Intel
  • 3Karlsruhe Institute of Technology
  • 4Karlsruher Institut für Technologie
  • 5Specialized Bicycle Components
  • 6Specialized Bicycles

Details

Category

Workshop

Sessions

09:00 - 13:00 | Sun 9 Jun | Room L109 | SuET6

CIV: Cooperative Interacting Vehicles

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Abstract

Recent accidents on roads involving cyclists and autonomous vehicles have raised an alarm in the industry to focus more on cyclist safety. Although there are plenty of datasets publicly available in the industry, they don't include enough instances of cyclists in different road conditions to run comprehensive tests. Therefore, in this work, we present a new publicly available Specialized Cyclist Dataset, which focuses solely on cyclist detection. Our dataset was recorded using a monocular RGB camera in various scenarios experienced by cyclists on roads in Autumn and Winter (with snow) for enabling researchers to run rigorous tests in various conditions. There are 62297 total images, about 18200 cyclists instances, and 30 different cyclists. Additionally in the dataset, we present the Specialized cyclist jersey with a diamond pattern designed specifically for improving detection accuracy compared to street clothes. For convenience, we utilized the popular KITTI labeling format and resolution in addition to Full HD resolution.

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