Real-Time Point Cloud Action Recognition System with Automated Point Cloud Preprocessing

Yen Ting Lai*, Cheng Hung Lin, Po Yung Chou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Point cloud action recognition has the advantage of being less affected by changes in lighting and viewing angle, as it focuses on the three-dimensional position of an object rather than pixel values. This enables robust recognition performance even in complex and dark environments. Additionally, point cloud action recognition finds widespread applications in fields such as robotics, virtual reality, autonomous driving, human-computer interaction, and game development. For instance, understanding human actions is crucial for better interaction and collaboration in robotics, while in virtual reality, it can capture and reproduce user movements to enhance realism and interactivity. To build a smoothly operating point cloud action recognition system, it is often necessary to filter out background and irrelevant points, resulting in clean and aligned data. In previous methods, point cloud filtering and action recognition were usually performed separately, with fewer systems operating together or action recognition without background filtering. In this paper, we propose a pipeline that enables users to directly acquire point cloud data from the Azure Kinect DK and perform comprehensive automated preprocessing. This generates cleaner point cloud data without background points, suitable for action recognition. Our approach utilizes PSTNet for point cloud action recognition and trains the model on the dataset obtained through automated preprocessing, which includes 12 action classes. Finally, we have developed a real-time point cloud action recognition system that combines automated point cloud preprocessing.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Consumer Electronics, ICCE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350324136
DOIs
Publication statusPublished - 2024
Event2024 IEEE International Conference on Consumer Electronics, ICCE 2024 - Las Vegas, United States
Duration: 2024 Jan 62024 Jan 8

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
ISSN (Print)0747-668X
ISSN (Electronic)2159-1423

Conference

Conference2024 IEEE International Conference on Consumer Electronics, ICCE 2024
Country/TerritoryUnited States
CityLas Vegas
Period2024/01/062024/01/08

Keywords

  • action recognition
  • dynamic point cloud

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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