ML Anthology

Live Demonstration: Real-Time Event-Based Speed Detection Using Spiking Neural Networks

Arjun Roy, Manish Nagaraj, Chamika Mihiranga Liyanagedera, Kaushik Roy
CVPRW 2023 pp. 4081-4082
doi:10.1109/CVPRW59228.2023.00428 /cvprw/2023/roy2023cvprw-live/

Abstract

Event cameras are emerging as an ideal vision sensor for high-speed applications due to their low latency and power consumption. DOTIE, a recent work in literature, has proposed a method to detect objects through spatial and temporal isolation of events with a spiking neural network. In this work, we implement DOTIE to detect a disk moving in a circular motion and identify the speed of rotation. We further validate the claim that spiking architectures can efficiently handle events by implementing DOTIE on Intel Loihi, a neuromorphic hardware suitable for spiking neural networks, and reveal a 14× reduction in energy consumption compared to the CPU implementation of DOTIE.

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Cite

Text

Roy et al. "Live Demonstration: Real-Time Event-Based Speed Detection Using Spiking Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00428

Markdown

[Roy et al. "Live Demonstration: Real-Time Event-Based Speed Detection Using Spiking Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/roy2023cvprw-live/) doi:10.1109/CVPRW59228.2023.00428

BibTeX

@inproceedings{roy2023cvprw-live,
  title     = {{Live Demonstration: Real-Time Event-Based Speed Detection Using Spiking Neural Networks}},
  author    = {Roy, Arjun and Nagaraj, Manish and Liyanagedera, Chamika Mihiranga and Roy, Kaushik},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {4081-4082},
  doi       = {10.1109/CVPRW59228.2023.00428},
  url       = {https://mlanthology.org/cvprw/2023/roy2023cvprw-live/}
}