Detecting Hands in Egocentric Videos: Towards Action Recognition
This work addresses hand detection for wearable camera users, but it is incremental as it builds on existing methods with modest improvements.
The paper tackled the problem of detecting hands in egocentric videos to aid action recognition, addressing challenges like illumination changes and hand appearance variability, and achieved competitive results on the UNIGE-HANDS dataset.
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.