While several datasets for autonomous navigation have become available in recent years, they have tended to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in object or background appearance and strong adherence to traffic rules. We propose a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 20,000 images, finely annotated with 34 classes collected over 200 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes.

The challenge will feature
  • Datasets for segmentation, localization.
  • More than 20,000 annotated images hand picked from unstructured environments and occurrence of rare events.
  • More associated data including nearby frames, LIDAR data, GPS which can be used for specific challenges.
  • Challenges for resource constrained models with specific runtime budgets.

If you are interested in participating, please register here: Click Here

Challenge

  • Challenge Opening: Datasets for Localization and evaluation scripts released. Datasets for Segmentation have been released partially (15K available now). Full data release by August 17. See benchmarks for more info.
  • Challenge Final Submission: September 20, 2019
  • Result Announcement: September 23, 2019

Prizes

Intel is sponsoring prize money of 1000USD for the winners of each of the 4 challenges.

Organizers

iiit-H


intel
ucsd
uutah