Summary of Streamtinynet: Video Streaming Analysis with Spatial-temporal Tinyml, by Hazem Hesham Yousef Shalby et al.
StreamTinyNet: video streaming analysis with spatial-temporal TinyML
by Hazem Hesham Yousef Shalby, Massimo Pavan, Manuel Roveri
First submitted to arxiv on: 22 Jul 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 A novel architecture for Tiny Machine Learning (TinyML) is introduced, specifically designed to perform video streaming analysis (VSA) on resource-constrained devices like Internet of Things (IoT) devices. The current solutions rely on frame-by-frame analysis, missing the opportunity to leverage temporal patterns in the data stream. StreamTinyNet, a pioneering work in TinyML, addresses this limitation by enabling multiple-frame VSA. This architecture is tested on public datasets and demonstrates its effectiveness and efficiency. Moreover, it has been successfully ported to the Arduino Nicla Vision, showcasing its feasibility for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary StreamTinyNet is a new way of using tiny machine learning (TinyML) to analyze videos on small devices like smart cameras or phones. Currently, these devices can only look at one frame at a time, which limits what they can do. The new system can look at multiple frames and find patterns that weren’t possible before. This means it could be used for things like recognizing actions in sports games or monitoring traffic flow. |
Keywords
* Artificial intelligence * Machine learning
