Virtualot — A Framework Enabling Real-Time Coordinate Transformation & Occlusion Sensitive Tracking Using UAS Products, Deep Learning Object Detection & Traditional Object Tracking Techniques
Bradley J. Koskowich, Maryam Rahnemoonfai, Michael J. Starek · 2018
In this work we explore a combination of methods that allow us to analyze and study hyper-local environmental phenomena. Developing a unique application of monoplotting enables visualization of the results of deep-learning object detection and traditional object tracking processes applied to a perspective view of a parking lot on aerial imagery in realtime. Additionally, we propose a general algorithm to extract some scene understanding by inverting the monoplotting process and applying it to digital elevation models. This allows us to derive estimations of perspective image areas causing object occlusions. Connecting the real world and perspective spaces, we can create a resilient object tracking environment using both coordinate spaces to adapt tracking methods when objects encounter occlusions. We submit that this novel composite of techniques opens avenues for more intelligent, robust object tracking and detailed environment analysis using GIS in complex spatial domains provided video footage and UAS products.