Pedestrian detection at night from RGB camera is an under-represented yet very important problem, where current state-of-the-art vision algorithms fail. Computer vision methods for detection at night have not received much attention, despite the fact they are a critical building block of many systems such as safe and robust autonomous cars.
To further assess and advance the state of the art, we organize the NightOwls Pedestrian Detetection Challenge 2019, as part of the Computer Vision for Road Scene Understanding and Autonomous Driving (CVRSUAD) Workshop at ICCV 2019.
The competition uses the recently published NightOwls dataset, consisting of 279,000 fully-annotated images in 40 video sequences recorded at night across 3 different countries by an industry-standard camera. Participants are encouraged to train their models on the training subset (128k images), tune the hyper-parameters on the validation subset (48k) and then submit their detection results on the testing subset (128k images). Data annotations are available for the training/validation subset, but the annotations for the testing set will only be published after the competition ends, to ensure fair competition.
The winner will be announced at the CVRSUAD workshop, ICCV 2019 on 27th October and will be presented with valuable and unique prices, by courtesy of Visual Geometry Group, University of Oxford :) After that, the submission site will switch to continous mode.
Methods ranking is based on the standard Average Miss Rate metric used in the pedestrian detection literature , considering only targets within the Reasonable  set up (i.e. non-occluded targets with height >= 50px). The winning entry will be the method with the lowest Average Miss Rate.
The server expects a single ZIP archive with a single JSON file inside. 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
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:
Start: Sept. 1, 2019, midnight
Start: Sept. 30, 2019, midnight
Oct. 13, 2019, 11:59 p.m.
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