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  1. University of Computer Studies, Yangon
  2. Faculty of Information Science

Effective Multi-View for Human Activity Recognition on Skeletal Model

http://hdl.handle.net/20.500.12678/0000004578
http://hdl.handle.net/20.500.12678/0000004578
91ebdd17-ec51-4708-bcdf-f73c5c23c9aa
0f7fdfdd-eb0f-48f6-bed4-48f8dc99ffc0
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SandarWin.pdf SandarWin.pdf (498 Kb)
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Article
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Publication
Title
Title Effective Multi-View for Human Activity Recognition on Skeletal Model
Language en
Publication date 2020-02-28
Authors
Win, Sandar
Thein, Thin Lai Lai
Description
The recognition of 3D human pose from 2D joint location is fundamental to numerous visionissues in analysis of video sequences. Various methods using with skeletal model have been described in pastdecades, but there is required a powerful system with stable and reliable manner in activity recognitionbecause video sequences can contain different people that may be any position or scale and complex spatialinterference. With the development of deep learning, skeleton-based human representation is more reliableto motion speed and appearance of human body scale. Skeleton data contains compact information of themajor body joints and that support multi-view to human activity recognition. To satisfy our aim, the proposedsystem is developed by using OpenPose detector that achieve effective results for 2D pose and DeepLearning based approach. Our goal is to extract valuable information between human joints and to recognizecorrect activity from human representation in video sequences.
Keywords
OpenPose, Human Activity Recognition, Deep Learning
Identifier 978-981-14-4787-7
Journal articles
Proceedings of the 10th International Workshop on Computer Science and Engineering (WCSE 2020)
Conference papers
Books/reports/chapters
Thesis/dissertations
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