Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.01281 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913268596998144 |
|---|---|
| author | Jatla, Venkatesh Teeparthi, Sravani Egala, Ugesh Pattichis, Sylvia Celedon Patticis, Marios S. |
| author_facet | Jatla, Venkatesh Teeparthi, Sravani Egala, Ugesh Pattichis, Sylvia Celedon Patticis, Marios S. |
| contents | Research on video activity detection has primarily focused on identifying well-defined human activities in short video segments. The majority of the research on video activity recognition is focused on the development of large parameter systems that require training on large video datasets. This paper develops a low-parameter, modular system with rapid inferencing capabilities that can be trained entirely on limited datasets without requiring transfer learning from large-parameter systems. The system can accurately detect and associate specific activities with the students who perform the activities in real-life classroom videos. Additionally, the paper develops an interactive web-based application to visualize human activity maps over long real-life classroom videos. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Low-parameter Video Activity Localization in Collaborative Learning Environments Jatla, Venkatesh Teeparthi, Sravani Egala, Ugesh Pattichis, Sylvia Celedon Patticis, Marios S. Computer Vision and Pattern Recognition Artificial Intelligence Research on video activity detection has primarily focused on identifying well-defined human activities in short video segments. The majority of the research on video activity recognition is focused on the development of large parameter systems that require training on large video datasets. This paper develops a low-parameter, modular system with rapid inferencing capabilities that can be trained entirely on limited datasets without requiring transfer learning from large-parameter systems. The system can accurately detect and associate specific activities with the students who perform the activities in real-life classroom videos. Additionally, the paper develops an interactive web-based application to visualize human activity maps over long real-life classroom videos. |
| title | Fast Low-parameter Video Activity Localization in Collaborative Learning Environments |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2403.01281 |