Alex Wong

University of Waterloo
Project Title: Efficient deep learning for real-time traffic event detection
Industry Partner: Miovision
Platform: Agile Computing, Large Memory System

Digital Media Cities

SOSCIP Project Summary (provided by researcher)

Miovision is interested in designing the first affordable, low-power, energy efficient real- time traffic event detection system that can be installed without the need to be powered by the grid nor the need to be connected directly to city installed infrastructure. Deep learning for traffic event detection can provide overwhelmingly superior accuracy and addresses most of the real-world scenarios that make competing detectors unsuitable for customer adoption. The challenge with deep learning is its complexity, which is currently infeasible for a self-powered real-world embedded detection system. Working with Dr. Alexander Wong and the Vision and Image Processing Lab at the University of Waterloo, the goal of this project is to develop technologies that can significantly reduce the complexity of deep learning for traffic event detection, while maintaining its accuracy and market fit, so that it can be deployed on a low-cost and low-powered hardware platform.