roi-approaches-in-har
Code for HAR paper 'Comparative Analysis of Adaptive ROI Approaches in Human Action Recognition'
Science Score: 57.0%
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Low similarity (9.8%) to scientific vocabulary
Repository
Code for HAR paper 'Comparative Analysis of Adaptive ROI Approaches in Human Action Recognition'
Basic Info
- Host: GitHub
- Owner: wtepsan
- Language: Python
- Default Branch: main
- Size: 297 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Adaptive Body Part ROI with Triple Stream Approach
This repository contains the code implemented as part of the research for the paper titled "Adaptive Body Part ROI with Triple Stream Approach for Human Action Recognition," authored by Worawit Tepsan, Sitapa Watcharapinchai, Pitiwat Lueangwitchajaroen, and Sorn Sooksatra.
Abstract
This study explores Adaptive Regions of Interest (ROI) methods in Human Action Recognition (HAR) through the utilization of OpenPose keypoints for ROI image generation from video data. Utilizing the NTU RGB+D 60 dataset and the EfficientNetB7 model, we examine ROIs ranging from full-body to specific joint segmentations. We propose a Triple Stream approach—where each stream employs a unique ROI image generation process. Our results demonstrate that the Triple Stream approach, combining Full Body Segmentation, 7 Joint ROI, and 6 Joint ROI, significantly enhances HAR accuracy for the XSUB benchmark. Similarly, for the XVIEW benchmark, a combination of Full Body Segmentation, 7 Joint ROI, and 3 Joint ROI significantly improves accuracy. Our proposed approach can also be adapted to enhance the performance of other models. Notably, by integrating the Triple Stream approach with alterations to the RGB channel in MMNet \cite{MMnet}, we achieve accuracies of 97.2\% on the XSUB benchmark and 99.3\% on XVIEW.
Download Paper
LINK: https://doi.org/10.1109/JCSSE61278.2024.10613745
System Requirements
- Python 3.9
Code Implementation
To implement the code, you will need to download dataset, pretrained models and set up paths properly. I will futher add some details later. So sorry for an inconvenience.
Citation
If you use this code or our findings in your research, please cite our paper as follows:
```bibtex @INPROCEEDINGS{10613745, author={Tepsan, Worawit and Sooksatra, Sorn and Lueangwitchajaroen, Pitiwat and Watcharapinchai, Sitapa}, booktitle={2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)}, title={Adaptive Body Part ROI with Triple Stream Approach for Human Action Recognition}, year={2024}, volume={}, number={}, pages={79-85}, keywords={Image segmentation;Adaptation models;Accuracy;Image synthesis;Training data;Streaming media;Benchmark testing;─Human Action Recognition;ROI}, doi={10.1109/JCSSE61278.2024.10613745} }
Owner
- Name: Worawit Tepsan
- Login: wtepsan
- Kind: user
- Repositories: 1
- Profile: https://github.com/wtepsan
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Tepsan
given-names: Worawit
affiliation: "International College of Digital Innovation, Chiang Mai University, Thailand"
email: worawit.tepsan@cmu.ac.th
title: "Identifying Product Growth Poles through Analysis of Transportation and Production Data: A Case Study of Longan in the Upper Northern Region of Thailand"
url: "https://github.com/wtepsan/ROI-Approaches-in-HAR/"
date-released: 2024-06-30
preferred-citation:
type: article
authors:
- family-names: Tepsan
given-names: Worawit
affiliation: "International College of Digital Innovation, Chiang Mai University, Thailand"
email: worawit.tepsan@cmu.ac.th
doi: "10.0000/00000"
journal: "Journal Title"
month: 9
start: 1 # First page number
end: 6 # Last page number
title: "Identifying Product Growth Poles through Analysis of Transportation and Production Data: A Case Study of Longan in the Upper Northern Region of Thailand"
issue: 1
volume: 1
year: 2024