The flagship dataset to emerge from the Transportation Research Board's second Strategic Highway Research Program (SHRP 2) study is the naturalistic driving study (NDS). The study is a focus on driver behavior and addresses the notion that it is possible to obtain more and better information on what people do when they drive--not just in the moments before they get into a collision but on a day-to-day basis. Researchers for the NDS have gathered over 1.2-million hours of data, collected from the vehicles of approximately 3,000 volunteers going about their regular activities. Each of those vehicles was equipped with four cameras, a Global Positioning System (GPS), and many other sensors. Over 2 petabytes (2,000 terabytes) of data has been generated over a 2-year period, a majority of which comes from video captured by the onboard cameras. The massive size of the video data creates a serious data bottleneck for researchers and makes traditional methods for identifying features in the data, such as objects, behaviors, roadside design details, and surrounding vehicles, completely inadequate. Several video analytics research projects, funded by the EAR Program, aim to make data processing and analysis more practical by focusing on increasing the automation of video data decoding. The goal of these EAR Program projects is to assist researchers through the use of an algorithm designed to narrow down the amount of data researchers need to manually review. There are six ongoing video analytics projects funded by the EAR Program, all tasked with developing technology to make it feasible for a researcher to quickly and flexibly get what they need from an extremely large dataset. The following pages contain summary descriptions of the EAR Program-sponsored research projects investigating video analytics.