An emphasis on active modes of transportation, that is, walking and cycling, has recently been renewed amid concerns for the environment and public health. However, the focus of research and practice that these modes have traditionally received is secondary to that received by motorized modes. As a consequence, the data on pedestrians (in particular, microscopic data) required for analysis and modeling are lacking. For instance, accurate data on the length of individual stride are not available in the transportation literature. This paper proposes a simple method to extract frequency and length of pedestrian stride automatically from video data collected nonintrusively in outdoor urban environments. The walking speed of a pedestrian oscillates during each stride; the oscillation can be identified through the frequency analysis of the speed signal. The method was validated with real-world data collected in Rouen, France, and Vancouver, Canada, where the root mean square errors for stride length were 6.1 and 5.7 cm, respectively. A method to distinguish pedestrians from motorized vehicles is proposed and used to analyze the 50 min of the Rouen data set to provide the distributions of stride frequency and length.