Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition

TL;DR

This paper explores how incorporating cultural information into vision-based human activity recognition systems can improve their accuracy, demonstrating that culture-aware models outperform culture-unaware ones in recognizing daily activities.

Contribution

It introduces four solutions for integrating cultural data into activity recognition and presents a Naive Bayes system that associates cultural and semantic image information.

Findings

Culture-aware solutions are more accurate than culture-unaware ones.

Preliminary results show promise for culture-aware activity recognition.

The proposed system is a promising starting point for further development.

Abstract

Daily life activities, such as eating and sleeping, are deeply influenced by a person's culture, hence generating differences in the way a same activity is performed by individuals belonging to different cultures. We argue that taking cultural information into account can improve the performance of systems for the automated recognition of human activities. We propose four different solutions to the problem and present a system which uses a Naive Bayes model to associate cultural information with semantic information extracted from still images. Preliminary experiments with a dataset of images of individuals lying on the floor, sleeping on a futon and sleeping on a bed suggest that: i) solutions explicitly taking cultural information into account are more accurate than culture-unaware solutions; and ii) the proposed system is a promising starting point for the development of culture-aware Human Activity Recognition methods.