With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams

TL;DR

This paper presents a method to automatically detect social interactions from low frame rate egocentric photos by analyzing distance and orientation features with an LSTM network, achieving promising results.

Contribution

It introduces a novel approach inspired by F-formation to detect social interactions using distance and orientation features in egocentric photo-streams.

Findings

Effective detection of social interactions in egocentric images.

LSTM-based model achieves promising accuracy.

Analyzed over a dataset of 30,000 images.

Abstract

Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.