A novel approach for automatic annotation of human actions in 3D point clouds for flexible collaborative tasks with industrial robots.

ABSTRACT: Manual annotation for human action recognition with content semantics using 3D Point Cloud (3D-PC) in industrial environments consumes a lot of time and resources. This work aims to recognize, analyze, and model human actions to develop a framework for automatically extracting content semantics. Main Contributions of this work: 1. design a multi-layer structure of various DNN classifiers to detect and extract humans and dynamic objects using 3D-PC preciously, 2. empirical experiments with over 10 subjects for collecting datasets of human actions and activities in one industrial setting, 3. development of an intuitive GUI to verify human actions and its interaction activities with the environment, 4. design and implement a methodology for automatic sequence matching of human actions in 3D-P

SUBMITTER: Krusche S 

PROVIDER: S-EPMC9975387 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature