The objective of this research is to create a suite of tools for monitoring airport gate activities with the objective of improving aircraft turnaround. Airport ramp areas are the most crowded and cluttered spaces in the entire National Airspace System (NAS). Activities related to turnaround of the aircraft from the gate represent a significant source of delay and therefore impact the predictability of NAS operations. Optimal Synthesis Inc., seeks to leverage its expertise in monitoring aircraft in the ramp areas using video surveillance data and advanced computer vision algorithms towards building an advanced gate activity monitoring that will in turn enable a gate turnaround prediction tool. The tool suite will specifically identify the various stages of turnaround such as refueling, luggage unloading/loading, catering, and deicing. It will further create a probabilistic model of the times associated with each of these events, that will be used for predicting the future sequence of events and their predicted times of completion. Phase I research will demonstrate the core ideas of gate activity recognition using state-of-the-art computer vision and machine learning algorithms. Phase II research will elevate the technology readiness level of this tool suite to work with real-time video surveillance streams.