Visual Lifelogs Retrieval: State of the Art and Future Challenges
Date Issued
November 4, 2019
Author(s)
DOI
10.1109/SMAP.2019.8864803
Abstract
The use of wearable cameras covers several areas of application nowadays, where the need for developing smart applications providing the sustainability and well-being of citizens it is more necessary than ever before. The tremendous amount of lifelogging data to extract valuable knowledge about the every day life of the wearers requires state of the art retrieval techniques to efficiently store, access, search and retrieve useful information. Several works have been proposed combining computer vision and machine learning techniques to analyze the content of the data captured from visual wearable devices on a daily basis. This paper presents an overview of the progress in visual lifelogging retrieval and indicates the current advances and future challenges, highlighting the prospects of incorporating visual lifelogging retrieval in social computing applications.