Summary of Fast Low-parameter Video Activity Localization in Collaborative Learning Environments, by Venkatesh Jatla et al.
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments
by Venkatesh Jatla, Sravani Teeparthi, Ugesh Egala, Sylvia Celedon Pattichis, Marios S. Patticis
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed system for video activity detection is designed to identify specific human activities in longer videos, unlike previous research that focused on short segments. This low-parameter, modular approach uses rapid inferencing and can be trained on limited datasets without requiring transfer learning from larger systems. The system is tested using real-life classroom videos and accurately detects the activities of students, even in long videos. To facilitate analysis, an interactive web-based application is developed to visualize human activity maps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to detect people doing things in longer videos. Usually, researchers focus on short clips and big computer models that need lots of training data. The new system is different because it’s fast, small, and can learn from smaller datasets. It works well with real-life classroom videos, showing which students are doing what activities. To make it easier to understand, the team created a special online tool to map out human activity in long videos. |
Keywords
» Artificial intelligence » Transfer learning
