Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorXiong, Jingxien_US
dc.identifier.citationXiong, J. (2022). Image analytics using Artificial Intelligence (Human Action Recognition in industrial workplace). Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractThe recent pandemic has reinforced the concept of industry 4.0 in traditional manufacturer industries, and one of the rising needs is to understand operators’ action to increase productivity and efficiency. Compared to traditional video action recognition tasks, video cation recognition under an industrial setting involves unusual objects, complex background and more inter-human interactions, which have an obvious gap between current public action recognition dataset. In this project, an industrial based dataset is being constructed to fill the blank in action recognition tasks in industrial workplace. Furthermore, two methods are proposed to improve the existing TSN and TSM model performance on human action recognition tasks via introducing the concept of grouping and split-attention mechanism to enhance model efficiency and accuracy. Various experiment setting and data augmentation methods are also reviewed in detail to explore the optimum setting in action recognition tasks. The model performance has improved from 78.80% to 90.51% on UCF101 dataset, and has reached 84.22% accuracy on self- constructed industrial dataset.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleImage analytics using Artificial Intelligence (Human Action Recognition in industrial workplace)en_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorYap Kim Huien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
FYP_Report_Xiong Jingxi_Video_Action_Recognition vF.pdf
  Restricted Access
1.51 MBAdobe PDFView/Open

Page view(s)

Updated on Aug 13, 2022


Updated on Aug 13, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.