Video Detection and Classification of Pedestrian Events at Roundabouts and Crosswalks

A well-established technique for studying pedestrian safety is based on reducing data from video-based in-situ observation. The extraction and cataloging from recorded video of pedestrian crossing events has largely been achieved manually. Although the manual methods are generally reliable, they are extremely time-consuming. As a result, more detailed, encompassing site studies are not practical unless the mining for these events can be automated. The study investigated such a tool based on utilizing a novel image processing algorithm recently developed for the extraction of human activities in complex scenes. No human intervention other than defining regions of interest for approaching vehicles and the pedestrian crossing areas was required. The output quantified general event indicators—such as pedestrian wait time, and crossing time and vehicle-pedestrian yield behaviors. Such data can then be used to guide more detailed analyses of the events to study potential vehicle-pedestrian conflicts and their causal effects. The evaluation was done using an extensive set of multi-camera video recordings collected at roundabouts. The tool can be used to support other pedestrian safety research where extracting potential pedestrian-vehicle conflicts from video are required, for example at crosswalks at urban signalized and uncontrolled intersections. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. This report does not necessarily reflect the official views or policies of the University of Minnesota. The authors, the University of Minnesota, and the U.S. Government do not endorse products or manufacturers. Any trade or manufacturers' names that may appear herein do so solely because they are considered essential to this report. Acknowledgment The authors would like to acknowledge individuals and organizations that made this research possible. (UTC) program. The authors would also like to give special acknowledgement to the Minnesota Traffic Observatory within the Department of Civil Engineering for access to their roundabout video database of pedestrian and vehicle activities .