Pedestrian data collection is critical for the planning and design of pedestrian facilities. Most pedestrian data collection efforts involve field observations or observer-based video analysis. These manual observations are time consuming, limited in coverage, resource intensive and error prone. Automated video analysis which involves the use of computer vision techniques can overcome many of these shortcomings. Despite advances in the field of computer vision applications for pedestrian detection and tracking, the technical literature shows little use of these techniques in pedestrian data collection practices. The likely reasons are the technical complexities that surround the processing of pedestrian videos. To extract pedestrian trajectories automatically from video, all road users must be detected, tracked at each frame and classified by type, at least as pedestrians and non-pedestrians. This is a challenging task in busy open outdoor urban environment. Common problems include global illumination variations, multiple object tracking and shadow handling. Specific problems arise when dealing with pedestrians because of their complex movement dynamics, varied appearance and non-rigid nature. The main objective of this study is to present a system for automated collection of pedestrian walking speed using