Detecting Pedestrian Spatial Behavior in City Spaces by Processing 360° Videos

This study developed a framework based on deep learning algorithms for processing 360° videos for detecting pedestrian spatial behavior in urban spaces. Information divergence is determined through the sampling and conversion of spatiotemporal behavior data for pedestrian flow analysis. Traditional videos, such as those captured by one-way security cameras, cannot be used to fully analyze the flow of pedestrians in cities. Therefore, a 360° camera is used to capture panoramic videos of city spaces over time. Subsequently, deep learning algorithms are used to process the videos and obtain pedestrian trajectory data for analyzing their spatial behavior and interactions. The results of real-world implementation indicate that the proposed method and analytical framework can be used to detect pedestrians and collect data related to pedestrians’ spatial behavior. However, the sampling rate and application of pedestrians’ trajectory data must be explored in future studies.