Skeleton-based Approaches based on Machine Vision: A Survey

TL;DR

This survey comprehensively reviews skeleton-based approaches in machine vision, emphasizing their application fields and tasks, to enhance understanding and guide future research in this area.

Contribution

It provides the first thorough analysis of skeleton-based methods focused on application fields and tasks, rather than just theoretical aspects.

Findings

Summarizes various skeleton-based approaches across different application domains.

Highlights the importance of skeleton features in solving specific vision problems.

Identifies gaps and future directions in skeleton-based machine vision research.

Abstract

Recently, skeleton-based approaches have achieved rapid progress on the basis of great success in skeleton representation. Plenty of researches focus on solving specific problems according to skeleton features. Some skeleton-based approaches have been mentioned in several overviews on object detection as a non-essential part. Nevertheless, there has not been any thorough analysis of skeleton-based approaches attentively. Instead of describing these techniques in terms of theoretical constructs, we devote to summarizing skeleton-based approaches with regard to application fields and given tasks as comprehensively as possible. This paper is conducive to further understanding of skeleton-based application and dealing with particular issues.