Competition

NightOwls Detetection Challenge 2020

Organized by lukeN86 - Current server time: May 31, 2020, 6:27 a.m. UTC

Previous

Training/Validation Phase
Jan. 1, 2020, midnight UTC

Current

Testing Phase
May 25, 2020, midnight UTC

End

Competition Ends
June 7, 2020, 11:59 p.m. UTC

NightOwls Detetection Challenge 2020

Scalability in Autonomous Driving Workshop, CVPR 2020

Pedestrian detection at night is an under-represented yet important problem, where state-of-the-art detection algoritms fail, even when specifically trained with night-time data.

Following the success of the NightOwls Pedestrian Detection Challenge, ICCV 2019, we organize NightOwls Detetection Challenge 2020 as part of the Scalability in Autonomous Driving Workshop at CVPR 2020.

The NightOwls dataset and has 3 subsets - training, validation and testing. The training and validation subsets (images + annotations) are available below, the testing subset (images only) will be published at the beginning of the Testing phase of the competition. The number of submissions in the Testing phase is limited to 3.

The competition runs in three tracks.

  • Pedestrian Detection from a Single Frame (same as 2019 competition)
  • Pedestrian Detection from a Multiple Frames
  • All Objects Detection (pedestrian, cyclist, motorbike) from a Single Frame

The winners will be announced at the Scalability in Autonomous Driving Workshop, CVPR 2020.

Evaluation

Methods ranking is based on the standard Average Miss Rate metric used in the pedestrian detection literature [1], considering only targets within the Reasonable [1] set up (i.e. non-occluded targets with height >= 50px). The winning entry will be the method with the lowest Average Miss Rate. For Pedestrian - Single Frame and Pedestrian - Multi Frame tracks, we simply take the Average Miss Rate for the pedestrian class. For All Objects track, we average the Miss Rate over all classes, giving each object class an equal weight.

The server runs the same evaluation code from our NightOwls SDK, the evaluation can therefore easily also be run locally.

1. P. Dollár, C. Wojek, B. Schiele and P. Perona, Pedestrian Detection: A Benchmark, CVPR 2009, Miami, Florida

Terms and Conditions

This dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

  1. That the dataset comes “AS IS”, without express or implied warranty. Although every effort has been made to ensure accuracy, we (University of Oxford) do not accept any responsibility for errors or omissions.
  2. That you include a reference to the Nightowls Dataset in any work that makes use of the dataset.
  3. That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
  4. You may not use the dataset or any derivative work for commercial purposes such as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
  5. That all rights not expressly granted to you are reserved by us (University of Oxford).

Training/Validation Phase

Start: Jan. 1, 2020, midnight

Testing Phase

Start: May 25, 2020, midnight

Competition Ends

June 7, 2020, 11:59 p.m.

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