Juggling can be both a recreational activity that provides a wide variety of challenges to participants and an art form that can be performed on stage. Non-learning-based computer vision techniques, depth sensors, and accelerometers have been used in the past to augment these activities. These solutions either require specialized hardware or only work in a very limited set of environments. In this project, a 54 000 frame large video dataset of annotated juggling was created and a convolutional neural network was successfully trained that could locate the balls and hands with high accuracy in a variety of environments. The network was sufficiently light-weight to provide real-time inference on CPUs. In addition, the locations of the balls and hands were recorded for thirty-six common juggling pattern, and small neural networks were trained that could categorize them almost perfectly. By building on the publicly available code, models and datasets that this project has produced jugglers will be able to create interactive juggling games for beginners and novel audio-visual enhancements for live performances.