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

Arjun Roy, Manish Nagaraj, Chamika Mihiranga Liyanagedera, Kaushik Roy; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 4081-4082

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 14x reduction in energy consumption compared to the CPU implementation of DOTIE.

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[bibtex]
@InProceedings{Roy_2023_CVPR, author = {Roy, Arjun and Nagaraj, Manish and Liyanagedera, Chamika Mihiranga and Roy, Kaushik}, title = {Live Demonstration: Real-Time Event-Based Speed Detection Using Spiking Neural Networks}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {4081-4082} }