Animal-vehicle collision (AVC) is a significant safety issue on American roads. Each year approximately 1.5 million AVCs occur in the U.S., the majority of them involving deer. The increasing use of cameras and radar on vehicles provides opportunities for prevention or mitigation of AVCs, particularly those involving deer or other large animals. Developers of such AVC avoidance/mitigation systems require information on the behavior of encountered animals, setting characteristics, and driver response in order to design effective countermeasures. As part of a larger study, naturalistic driving data were collected in high AVC incidence areas using 48 participant-owned vehicles equipped with data acquisition systems (DAS). Continuous driving data including forward video, location information, and vehicle kinematics were recorded. The respective 11TB dataset contains 35k trips covering 360K driving miles. Collected naturalistic driving data were analyzed to characterize driver and animal behavior before and during animal-vehicle encounters (AVE), location and environmental characteristics, and estimated time-to-collision (TTC) using photogrammetric methods. Additional AVE data from three previously-existing naturalistic driving studies (NDS) were analyzed similarly with modifications for different video data formats. This paper presents only the methods used for data collection and photogrammetric data reduction and validation as a precursor to subsequent publications on the findings of the larger study.