Egocentric Human-Object Interaction Detection Exploiting Synthetic Data

TL;DR

This paper introduces a synthetic data generation pipeline for detecting egocentric human-object interactions in industrial settings, demonstrating improved detection performance with limited real data.

Contribution

The authors propose a novel synthetic data generation tool and pipeline for EHOI detection, along with a method that leverages this data to improve detection accuracy.

Findings

Synthetic data enhances EHOI detection performance.

Method performs well with limited real data.

Public dataset release supports further research.

Abstract

We consider the problem of detecting Egocentric HumanObject Interactions (EHOIs) in industrial contexts. Since collecting and labeling large amounts of real images is challenging, we propose a pipeline and a tool to generate photo-realistic synthetic First Person Vision (FPV) images automatically labeled for EHOI detection in a specific industrial scenario. To tackle the problem of EHOI detection, we propose a method that detects the hands, the objects in the scene, and determines which objects are currently involved in an interaction. We compare the performance of our method with a set of state-of-the-art baselines. Results show that using a synthetic dataset improves the performance of an EHOI detection system, especially when few real data are available. To encourage research on this topic, we publicly release the proposed dataset at the following url: https://iplab.dmi.unict.it/EHOI_SYNTH/.