MWNET: A Tracking Method For Frequently Occluded Scenes Based On Matter Waves

The industrial revolution has elevated science and engineering to foster the development of Image Processing and Artificial intelligence (AI) and put the visualization of information on an even higher pedestal. Yet, the demands of the industrial age have contributed to an ever-growing wildfire of climate change, sparking a revolution in energy efficiency research. With the aim to advance energy efficiency research from an AI standpoint, a novel transformation of raw-formatted data repositories, known as data lakes, into multi-dimensional visualizations data coupled with computationally lightweight, edge-based AI implementations are proposed as means to understand the energy consumption patterns in buildings. As a novel method of understanding energy data visually, current results comprise a Multi-Dimensional Gramian Angular Field (GAF) representation of energy data as both 2D and 3D interactive forms. Moreover, a case study on deep learning classification employed on ODROID-XU4 yields ~90% accuracy and a classification rate of 17.5 msec/image.