Pedestrian Flow Analysis Method in Public Spaces by Integrating Visual Information and Pedestrian Model

This paper proposed a method to improve the accuracy of pedestrian flow analysis by applying machine learning-based object detection to urban video data. To handle occlusions and maintain robust tracking, a pedestrian modeling approach was introduced, which allows detection even when objects overlap and enables class identification using past detection results. The effectiveness of the proposed method was verified in a real-world setting. It was confirmed that the method can achieve stable detection and reduce class assignment errors, achieving a mean absolute percentage error of 1.0% for pedestrians and 2.2% for bikes.