As video data collection and storage technologies become ubiquitous and inexpensive, transportation agencies struggle to process and extract "intelligence"Ã‚Â or useful information from growing libraries of archived video data. In some cases useful video is lost because agencies cannot justify the expensive staff time required to process it. This video information overload is caused by the inability of most transportation agencies to write customized video processing algorithms to extract valuable safety or traffic data from large amounts of collected raw video.
Manually watching long stretches of video to extract information is boring and expensive. In addition to being time and financially inefficient, human-based extraction of video data is prone to errors. Although there are some sophisticated, specialized applications for transportation agencies these are either/both proprietary or/and too expensive to be widely deployed.
In this project, we designed and implemented a user-friendly interface for pedestrian crossing and vehicle conflict detection, leveraging the OpenCV computer-vision libraries made available via open-source from Intel. The goal is to allow practitioners to more easily provide the semantics of the information they wish to extract and process into the video-processing algorithms. As a demonstration and development tool, we used a complicated mid-block crosswalk located on Southwest 4th Avenue. This is a busy downtown street on the Portland State University (PSU) campus. There are three lanes of heavy one-way motor vehicle traffic; speeds are moderate as most vehicles have just exited Interstate 405 (I-405). We used this location to demonstrate how easily and accurately our flexible, user-friendly tool can measure pedestrian wait times, crossing speeds, and near misses (e.g., a car abruptly stopping prior to the crosswalk or a vehicle passing a stopped vehicle). In addition, the system outputs meaningful data about vehicle and pedestrian trajectories for further analysis.