PEDESTRIAN FLOW ANALYSIS IN PUBLIC SPACE USING DEEP LEARNING

Abstract

As city vitalization through the utilization of public space is getting attention, the need for observation of human behavior in urban space is increasing. However, the analysis work relies on manual video observation, which requires an enormous amount of labor. In this study, we applied object detection and tracking technologies based on deep learning to develop a low-cost human flow analysis system, especially for outdoor urban spaces, which enables easy and fast analysis. Using the existing video data and human flow analysis data from the experiment in the Mitaka station area, we verified the analysis accuracy of the proposed method by setting "mesh" and "rounding threshold" for practical use in the developed system. As a result, we confirmed that the proposed method can grasp the trend of human flow in 1/4 of the analysis time of human survey. We also clarified issues for practical use, such as the method of camera installation and the necessity of image distortion correction.