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In recent times, there is an increased interest in the identification andre-identification of people at long distances, such as from rooftop cameras,UAV cameras, street cams, and others. Such recognition needs to go beyond faceand use whole-body markers such as gait. However, datasets to train and testsuch recognition algorithms are not widely prevalent, and fewer are labeled.This paper introduces DIOR -- a framework for data collection, semi-automatedannotation, and also provides a dataset with 14 subjects and 1.649 million RGBframes with 3D/2D skeleton gait labels, including 200 thousands frames from along range camera. Our approach leverages advanced 3D computer visiontechniques to attain pixel-level accuracy in indoor settings with motioncapture systems. Additionally, for outdoor long-range settings, we remove thedependency on motion capture systems and adopt a low-cost, hybrid 3D computervision and learning pipeline with only 4 low-cost RGB cameras, successfullyachieving precise skeleton labeling on far-away subjects, even when theirheight is limited to a mere 20-25 pixels within an RGB frame. On publication,we will make our pipeline open for others to use.

DIOR: Dataset for Indoor-Outdoor Reidentification -- Long Range 3D/2D  Skeleton Gait Collection Pipeline, Semi-Automated Gait Keypoint Labeling and  Baseline Evaluation Methods