Summary of Multi Class Activity Classification in Videos Using Motion History Image Generation, by Senthilkumar Gopal
Multi class activity classification in videos using Motion History Image generation
by Senthilkumar Gopal
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 paper presents a framework for capturing temporal and activity information using Motion History Images (MHI), which enables various applications including action classification. The authors demonstrate the effectiveness of MHI in producing sample data to train a classifier, achieving high accuracy across six different activities in a single multi-action video. They analyze the classifier’s performance and identify limitations where MHI struggles to generate suitable activity images, discussing potential mechanisms and future work to overcome these challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using pictures of movement to recognize human actions. This is important for things like security systems that need to track people in real-time, and gaming systems that rely on quick responses to gestures. The authors show how Motion History Images (MHI) can be used to capture the details of movement, which helps with tasks like classifying different activities. They use MHI to train a classifier and test it on six different actions in one video, showing that it works well. However, they also identify some limitations where MHI doesn’t work as well, and discuss ideas for how to improve it. |
Keywords
* Artificial intelligence * Classification
