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On the Role of Smart Vision Sensors in Energy-Efficient Computer Vision at the Edge

AuthorsAlberto Ancilotto; Francesco Paissan; Elisabetta Farella
TitleOn the Role of Smart Vision Sensors in Energy-Efficient Computer Vision at the Edge
AbstractThe increasing focus of the research community towards lightweight and small footprint neural network models is closing the gap between inference performance in cluster-scale models and tiny devices. In the recent past, researchers have shown how it is possible to achieve state-of-the-art performance in different domains (e.g. sound event detection, object detection, image classification) with small footprints and low computational cost architectures. However, these studies lack a comprehensive analysis of the input space used (e.g. for images) and present the results on standard RGB benchmarks. In this manuscript, we investigate the role of smart vision sensors (SVSs) in deep learning-based object detection pipelines. In particular, we combine the motion bitmaps with standard color spaces representations (namely, RGB, YUV, and grayscale) and show how SVSs can be used optimally for an IoT end-node. In conclusion, we report that, overall, the best-performing input space is grayscale augmented with the motion bitmap. These results are promising for real-world applications since many SVSs provide both image formats at low power consumption.
ISBN

978-1-6654-1647-4

Conference2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
Date25 March 2022
LocationOnline
Year of Publication, Publisher2022, IEEE
Urlhttps://zenodo.org/record/6839548#.YtFoeHZBy3B
DOI10.1109/PerComWorkshops53856.2022.9767380
  
  

Key Facts

  • Project Coordinator: Dr. Sotiris Ioannidis
  • Institution: Foundation for Research and Technology Hellas (FORTH)
  • E-mail: marvel-info@marvel-project.eu 
  • Start: 01.01.2021
  • Duration: 36 months
  • Participating Organisations: 17
  • Number of countries: 12

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Funding

eu FLAG

This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.