This project focuses on real-time object tracking of the Roosevelt Island Tram, using computer vision and machine learning algorithms for the image processing. In addition to exploring different techniques for real-time, accurate object detection, another major component of our project is considering how best to communicate tram location in a natural, useful way. While the tram is on a pretty regular schedule, sometimes this shifts based on day of week, number of trams running, and maintenance. Thus, we think there is value in providing the real world status of the tram given actual detection and verifications. Our target users are people, mostly students, on Cornell Tech campus. Given the group in consideration, the main problem to solve is planning when to leave campus to walk over to the tram. Thus, our visualizations of tram status will center on communicating to people on campus how much time they have left to walk to the tram through light, color, and numerical cues.
