Body and Head Orientation Estimation from Low-Resolution Point Clouds in Surveillance Settings

TL;DR

This paper introduces a privacy-preserving system using low-resolution LiDAR point clouds to accurately estimate body and head orientations in surveillance settings, enabling behavioral analysis without requiring close-range cameras.

Contribution

It presents the first low-resolution LiDAR-based head orientation estimation method suitable for surveillance, with models that outperform high-resolution methods in such settings.

Findings

Achieves mean absolute errors of 5.2° for body and 13.7° for head orientation.

Demonstrates significant behavioral differences between neurotypical and autistic individuals.

Validates the system's effectiveness in real-world surveillance scenarios.

Abstract

We propose a system that estimates people's body and head orientations using low-resolution point cloud data from two LiDAR sensors. Our models make accurate estimations in real-world conversation settings where subjects move naturally with varying head and body poses, while seated around a table. The body orientation estimation model uses ellipse fitting while the head orientation estimation model combines geometric feature extraction with an ensemble of neural network regressors. Our models achieve a mean absolute estimation error of 5.2 degrees for body orientation and 13.7 degrees for head orientation. Compared to other body/head orientation estimation systems that use RGB cameras, our proposed system uses LiDAR sensors to preserve user privacy, while achieving comparable accuracy. Unlike other body/head orientation estimation systems, our sensors do not require a specified close-range placement in front of the subject, enabling estimation from a surveillance viewpoint which produces low-resolution data. This work is the first to attempt head orientation estimation using point clouds in a low-resolution surveillance setting. We compare our model to two state-of-the-art head orientation estimation models that are designed for high-resolution point clouds, which yield higher estimation errors on our low-resolution dataset. We also present an application of head orientation estimation by quantifying behavioral differences between neurotypical and autistic individuals in triadic (three-way) conversations. Significance tests show that autistic individuals display significantly different behavior compared to neurotypical individuals in distributing attention between conversational parties, suggesting that the approach could be a component of a behavioral analysis or coaching system.