Woo-Sup Han, Il Song Han



Neuromorphic Convolutional Recurrent Neural Network for Road Safety or Safety Near the Road

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Neuromorphic visual processing inspired by the biological vision system of brain offers an intelligent vision system of new machine vision in everyday environment. With the growing interest for detecting moving objects on the road or roadside for enhancing the safety and security, the proposed neuromorphic visual processing was tested on vehicle’s blind spot cyclist or ramming vehicle terror attack. The neuromorphic convolutional-recurrent neural network has been proposed to detect target objects and demonstrated successfully, based on the saliency of neuromorphic visual processing without complex optimization of template matching. Neuromorphic features were processed by Autoencoder and simplified Gabor filter, and detected either blind spot cyclist on the bridge or ramming vehicle in CCTV video footage of terror attack. The consistent performance of either Gabor-like filters or the small Autoencoder filters demonstrated the feasibility of real-time and robust neuromorphic vision implemented by the small embedded system.



Neuromorphic visual processing, Deep neural networks, Vision, Cyclist detection, Car ramming detection, Crowd movement detection



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Cite this paper

Woo-Sup Han, Il Song Han. (2017) Neuromorphic Convolutional Recurrent Neural Network for Road Safety or Safety Near the Road. International Journal of Circuits and Electronics, 2, 74-78


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