Pedestrian tracking framework utilising computer vision for rapid analysis of public spaces

Abstract

The ability to record the trajectories and interactions of pedestrians in public places is necessary to understand, model and analyse the performance of built environments. However, few options are available to researchers to gather this information. Traditionally, simple point-counting techniques or video analysis performed with human sight have been relied upon to collect the required data, but these methods have limitations. Tracking the movements of pedestrians in public areas with point-counters can only reveal abstract flow patterns and lacks the potential to capture fine-grained detail. Human observation is useful for capturing the fine-grained detail of individual trajectories, but is rarely a tractable solution. Recent advances in computer vision have allowed for automatic pedestrian tracking and interaction capture in open public spaces. Here, we present a framework, based on existing technology that can be used to build a pedestrian tracking and trajectory analysis application which solves the tractability issues associated with human visual analysis. Our framework is especially useful for capturing the movements of pedestrians in open public spaces such as public transport platforms, which will provide researchers with a finer level of detail than previously possible. It should be noted that this framework is aimed at researchers who wish to perform post-processing analysis of recorded video, rather than those who wish to capture the data in real time