Detecting Hands in Egocentric Videos: Towards Action Recognition
Computer Vision and Pattern Recognition
2017-09-11 v1
Abstract
Recently, there has been a growing interest in analyzing human daily activities from data collected by wearable cameras. Since the hands are involved in a vast set of daily tasks, detecting hands in egocentric images is an important step towards the recognition of a variety of egocentric actions. However, besides extreme illumination changes in egocentric images, hand detection is not a trivial task because of the intrinsic large variability of hand appearance. We propose a hand detector that exploits skin modeling for fast hand proposal generation and Convolutional Neural Networks for hand recognition. We tested our method on UNIGE-HANDS dataset and we showed that the proposed approach achieves competitive hand detection results.
Cite
@article{arxiv.1709.02780,
title = {Detecting Hands in Egocentric Videos: Towards Action Recognition},
author = {Alejandro Cartas and Mariella Dimiccoli and Petia Radeva},
journal= {arXiv preprint arXiv:1709.02780},
year = {2017}
}