Dynamicity-based Crop-Drop: A Context-based 2D Pose Refreshing Algorithm

Sanket Nagnath Yerule, Yu Fu Wu, Chih Chung Kao, Yu Chee Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

2D pose estimation plays an important role in many activity recognition applications. However, pose estimation is computationally expensive. In this paper, we propose DCD (Dynamicity based Crop-Drop), a context-based pose refreshing algorithm with controllable information loss and much better processing speed. The main idea is to utilize the correlation between consecutive frames to understand the dynamicity of each individual pose and decide its pose refreshing rate to minimize pose information loss. Full pose estimation is only applied when needed. Our test results show up to twice the speed as compared to that of OpenPose with acceptable information loss when tested on multi-person, multi-dynamicity activities like sitting idle, practicing thai-qi, and dancing.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages258-263
Number of pages6
ISBN (Electronic)9780738142623
DOIs
StatePublished - Dec 2020
Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
CountryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • Activity recognition
  • computer vision
  • deep learning
  • human pose estimation

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