# Predicting Daily Activities From Egocentric Images Using Deep Learning

arXiv: https://arxiv.org/abs/1510.01576 · HTML: https://mlsanity.com/paper/1510.01576/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [Video-based Human Action Recognition using Deep Learning: A Review](https://mlsanity.com/paper/2208.03775/) (2022)
- [PAL: Intelligence Augmentation using Egocentric Visual Context Detection](https://mlsanity.com/paper/2105.10735/) (2021)
- [Reducing Annotation Burden in Physical Activity Research Using Vision-Language Models](https://mlsanity.com/paper/2505.03374/) (2025)

## Abstract (arXiv)

We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.