Gazing Into Missteps: Leveraging Eye-Gaze for Unsupervised Mistake Detection in Egocentric Videos of Skilled Human Activities

TL;DR

This paper introduces an unsupervised gaze-based method for mistake detection in egocentric videos of skilled activities, leveraging eye-gaze signals and a novel gaze completion task to identify deviations without labeled data.

Contribution

It proposes a new gaze completion approach and a mistake detection framework that does not require mistake annotations, outperforming supervised methods in various settings.

Findings

Achieves up to +14% accuracy gains in multiple datasets.

Effectively detects mistakes without labeled data.

Ranks first on the HoloAssist Mistake Detection challenge.

Abstract

We address the challenge of unsupervised mistake detection in egocentric video of skilled human activities through the analysis of gaze signals. While traditional methods rely on manually labeled mistakes, our approach does not require mistake annotations, hence overcoming the need of domain-specific labeled data. Based on the observation that eye movements closely follow object manipulation activities, we assess to what extent eye-gaze signals can support mistake detection, proposing to identify deviations in attention patterns measured through a gaze tracker with respect to those estimated by a gaze prediction model. Since predicting gaze in video is characterized by high uncertainty, we propose a novel gaze completion task, where eye fixations are predicted from visual observations and partial gaze trajectories, and contribute a novel gaze completion approach which explicitly models correlations between gaze information and local visual tokens. Inconsistencies between predicted and observed gaze trajectories act as an indicator to identify mistakes. Experiments highlight the effectiveness of the proposed approach in different settings, with relative gains up to +14%, +11%, and +5% in EPIC-Tent, HoloAssist and IndustReal respectively, remarkably matching results of supervised approaches without seeing any labels. We further show that gaze-based analysis is particularly useful in the presence of skilled actions, low action execution confidence, and actions requiring hand-eye coordination and object manipulation skills. Our method is ranked first on the HoloAssist Mistake Detection challenge.