  People detection methods are highly sensitive to the perpetual occlusions
among the targets. As multi-camera set-ups become more frequently encountered,
joint exploitation of the across views information would allow for improved
detection performances. We provide a large-scale HD dataset named WILDTRACK
which finally makes advanced deep learning methods applicable to this problem.
The seven-static-camera set-up captures realistic and challenging scenarios of
walking people.
  Notably, its camera calibration with jointly high-precision projection widens
the range of algorithms which may make use of this dataset. In aim to help
accelerate the research on automatic camera calibration, such annotations also
accompany this dataset.
  Furthermore, the rich-in-appearance visual context of the pedestrian class
makes this dataset attractive for monocular pedestrian detection as well,
since: the HD cameras are placed relatively close to the people, and the size
of the dataset further increases seven-fold.
  In summary, we overview existing multi-camera datasets and detection methods,
enumerate details of our dataset, and we benchmark multi-camera state of the
art detectors on this new dataset.
