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

TL;DR

This paper presents DIOR, a comprehensive dataset and semi-automated pipeline for long-range indoor-outdoor gait re-identification, enabling accurate 3D/2D skeleton labeling from minimal RGB data.

Contribution

It introduces DIOR, a large-scale gait dataset with novel semi-automated labeling methods for indoor and outdoor long-range scenarios, including a low-cost pipeline for outdoor skeleton annotation.

Findings

Achieved pixel-level accuracy in indoor motion capture-based labeling.

Successfully labeled far-away subjects with only 20-25 pixels in RGB frames.

Provided a large dataset with 1.649 million frames for gait re-identification research.

Abstract

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.