Single View Physical Distance Estimation using Human Pose
This enables existing camera systems to measure distances without calibration or sensors, useful for applications like social distancing, but it is incremental as it builds on pose-based methods.
The paper tackles the problem of estimating physical distances between people from a single RGB image or video by automating camera calibration and ground plane estimation using human pose priors, achieving state-of-the-art performance on public datasets.
We propose a fully automated system that simultaneously estimates the camera intrinsics, the ground plane, and physical distances between people from a single RGB image or video captured by a camera viewing a 3-D scene from a fixed vantage point. To automate camera calibration and distance estimation, we leverage priors about human pose and develop a novel direct formulation for pose-based auto-calibration and distance estimation, which shows state-of-the-art performance on publicly available datasets. The proposed approach enables existing camera systems to measure physical distances without needing a dedicated calibration process or range sensors, and is applicable to a broad range of use cases such as social distancing and workplace safety. Furthermore, to enable evaluation and drive research in this area, we contribute to the publicly available MEVA dataset with additional distance annotations, resulting in MEVADA -- the first evaluation benchmark in the world for the pose-based auto-calibration and distance estimation problem.