DIOR: Dataset for Indoor-Outdoor Reidentification -- Long Range 3D/2D
  Skeleton Gait Collection Pipeline, Semi-Automated Gait Keypoint Labeling and
  Baseline Evaluation Methods
Yuyang Chen Praveen Raj Masilamani Bhavin Jawade Srirangaraj Setlur Karthik Dantu
In recent times, there is an increased interest in the identification and
re-identification of people at long distances, such as from rooftop cameras,
UAV cameras, street cams, and others. Such recognition needs to go beyond face
and use whole-body markers such as gait. However, datasets to train and test
such recognition algorithms are not widely prevalent, and fewer are labeled.
This paper introduces DIOR -- a framework for data collection, semi-automated
annotation, and also provides a dataset with 14 subjects and 1.649 million RGB
frames with 3D/2D skeleton gait labels, including 200 thousands frames from a
long range camera. Our approach leverages advanced 3D computer vision
techniques to attain pixel-level accuracy in indoor settings with motion
capture systems. Additionally, for outdoor long-range settings, we remove the
dependency on motion capture systems and adopt a low-cost, hybrid 3D computer
vision and learning pipeline with only 4 low-cost RGB cameras, successfully
achieving precise skeleton labeling on far-away subjects, even when their
height is limited to a mere 20-25 pixels within an RGB frame. On publication,
we will make our pipeline open for others to use.