Today, Zensors, a Carnegie Mellon spinout and maker of cloud-based visual sensing technology is announcing the release of its latest deep learning technology, Car Pose Net. Previously, tracking rigid, three dimensional objects (like cars) using only single-view cameras was problematic. Car Pose Net fits 3D pose wireframes to cars, improving tracking results, especially in difficult conditions like snow or partial visual obstructions. This unlocks incredible potential for existing city and autonomous vehicle camera systems. Because the technology can be deployed using legacy camera hardware and Zensors edge or cloud compute platforms, more advanced, accurate, and real time traffic data can be unlocked.