Abstract
Describing people’s lifestyle has become a hot topic in the field of artificial intelligence. Lifelogging is described as the process of collecting personal activity data describing the daily behaviour of a person. Nowadays, the development of new technologies and the increasing use of wearable sensors allow to automatically record data from our daily living. In this paper, we describe our developed automatic tools for the analysis of collected visual data that describes the daily behaviour of a person. For this analysis, we rely on sequences of images collected by wearable cameras, which are called egocentric photo-streams. These images are a rich source of information about the behaviour of the camera wearer since they show an objective and first-person view of his or her lifestyle.
| Original language | English |
|---|---|
| Pages (from-to) | 1-4 |
| Number of pages | 4 |
| Journal | Electronic Letters on Computer Vision and Image Analysis |
| Volume | 19 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2020 |
Keywords
- Behaviour Understanding
- Computer Vision
- Egocentric Vision
- Food-scenes classification
- Image Classification
- Image Sequence Analysis
- Lifelogging
- Lifestyle Tracking
- Sentiment Retrieval
- Social patterns
- Temporal Segmentation
- Visual Pattern Recognition