Miovision generates traffic analytics on over 16,000 hours of video every week from over 50 countries around the world using an NVIDIA GPU cloud-based system. Using surveillance-quality video, our system combines a deep convolutional neural network (CNN) with quality assurance agents, who review, verify, and correct, as needed, the CNN results. Using this hybrid approach, we're able to provide customers with accurate traffic analytics, such as traffic volume, class, and movements, and apply agent feedback to identify which real-world environmental conditions, lighting conditions, or perspectives contribute to CNN mislabeling or missed vehicles, pedestrians, or bicycles. Using the human corrections, Miovision can retrain the CNN to continuously improve its accuracy. We'll describe our traffic analytics pipeline and the use of sparse CNN representations to achieve robust state-of-the-art accuracy at faster than real-time performance.