Feature Identification and Matching for Hand Hygiene Pose

TL;DR

This study compares SIFT, SURF, and ORB feature descriptors for hand hygiene pose recognition, finding ORB to be most efficient and accurate, with potential applications in automated hand hygiene monitoring.

Contribution

The paper evaluates and compares popular feature descriptors for hand hygiene pose recognition, highlighting ORB's superior performance for real-time applications.

Findings

ORB outperforms SIFT and SURF in correct matches and speed

ORB is suitable for real-time hand hygiene pose classification

OpenCV implementation facilitates practical application

Abstract

Three popular feature descriptors of computer vision such as SIFT, SURF, and ORB compared and evaluated. The number of correct features extracted and matched for the original hand hygiene pose-Rub hands palm to palm image and rotated image. An accuracy score calculated based on the total number of matches and the correct number of matches produced. The experiment demonstrated that ORB algorithm outperforms by giving the high number of correct matches in less amount of time. ORB feature detection technique applied over handwashing video recordings for feature extraction and hand hygiene pose classification as a future work. OpenCV utilized to apply the algorithms within python scripts.