GazeTarget360: Towards Gaze Target Estimation in 360-Degree for Robot Perception

TL;DR

GazeTarget360 is a novel system that estimates human gaze targets in 360-degree scenes from images, improving accuracy and reliability in real-world robot perception tasks.

Contribution

It introduces a new approach combining multiple inference engines for 360-degree gaze target estimation, addressing limitations of previous in-frame methods.

Findings

Accurately predicts gaze targets in unseen scenarios.

Outperforms prior methods in reliability and generalization.

First system to estimate gaze targets from realistic 360-degree footage.

Abstract

Enabling robots to understand human gaze target is a crucial step to allow capabilities in downstream tasks, for example, attention estimation and movement anticipation in real-world human-robot interactions. Prior works have addressed the in-frame target localization problem with data-driven approaches by carefully removing out-of-frame samples. Vision-based gaze estimation methods, such as OpenFace, do not effectively absorb background information in images and cannot predict gaze target in situations where subjects look away from the camera. In this work, we propose a system to address the problem of 360-degree gaze target estimation from an image in generalized visual scenes. The system, named GazeTarget360, integrates conditional inference engines of an eye-contact detector, a pre-trained vision encoder, and a multi-scale-fusion decoder. Cross validation results show that GazeTarget360 can produce accurate and reliable gaze target predictions in unseen scenarios. This makes a first-of-its-kind system to predict gaze targets from realistic camera footage which is highly efficient and deployable. Our source code is made publicly available at: https://github.com/zdai257/DisengageNet.