The mining industry is a significant contributor to the Canadian economy, requiring close to 100,000 people to be hired by 2020. However, mining remains the second most dangerous job across the planet with 46.9 fatalities per 100,000 workers. Until the recent MINER act, there is little interest in providing communication services in mines. The MINER act made tracking all coal miners mandatory. In this project, we aim to develop a smart computing platform that combines data from digital wireless radio, thermal and video images in real time. This platform will incorporate tracking algorithms to aid in precisely locating miners, explosives and vehicles in mines continuously. This is very difficult due to the very nature of the mines. Extremely harsh, irregularly confined, and rough mine environments make radio wave propagation unpredictable. Furthermore, low visible light conditions prevent effective surveillance using cameras. Increasing number of sensors helps accurately locate targets, but will also increase the complexity of the algorithms. Using a variety of sensors will require several algorithms to run simultaneously to extract and fuse useful information from noisy data gathered from the mine in real-time. Especially identification of moving object in low-light video and thermal imaging is computationally very intensive. Hence, such a platform can only be implemented on a supercomputer, or a network of very fast computers. For this purpose, our work will rely on SOSCIP’s extensive computational capabilities. The result of this work will lead to significant improvements in safety of the mine personnel and better resource allocation and management in the mine

Industry Partner(s):PBE Canada

Academic Institution:Ryerson University

Academic Researcher: Xavier Fernando

Focus Areas: Digital Media, Mining

Platforms: Cloud, GPU