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

Recently, there has been growing interest in the identification and re-identification of individuals from long distances using rooftop cameras, UAV cameras, street cams, and similar devices. This type of recognition extends beyond facial recognition, utilizing whole-body markers such as gait. However, datasets to train and test such recognition algorithms are scarce and often lack labeling. This paper introduces DIOR—a comprehensive framework for data collection, semi-automated annotation, and a dataset comprising 1.649 million RGB frames labeled with 3D/2D skeleton gait markers across 14 subjects. This dataset includes 200,000 RGB frames captured from long-range cam-eras. Our approach employs advanced 3D computer vision techniques to achieve pixel-level accuracy in indoor environments using motion capture systems. For outdoor, long-range environments, we eliminate the reliance on motion capture systems and implement a cost-effective, hybrid 3D computer vision and learning pipeline using only four inexpensive RGB cameras. This method successfully achieves precise skeleton labeling of distant subjects, even when their visual size is as small as 20-25 pixels within an RGB frame. We benchmark models trained on existing datasets such as CASIA-B, on our proposed dataset for the task of Gait recognition. Our pipeline and the accompanying dataset will be made publicly available following acceptance.