  The ability to extract the sequence of game events for a given player's
play-through has traditionally required access to the game's engine or source
code. This serves as a barrier to researchers, developers, and hobbyists who
might otherwise benefit from these game logs. In this paper we present two
approaches to derive game logs from game video via convolutional neural
networks and transfer learning. We evaluate the approaches in a Super Mario
Bros. clone, Mega Man and Skyrim. Our results demonstrate our approach
outperforms random forest and other transfer baselines.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning