Estimation of positions and velocities of vehicles and pedestrians from dashcam footage using monocular depth estimation

Abstract

To optimize traffic control and public transportation operations, and to implement effective safety measures, it is essential to monitor traffic and pedestrian flows in real time. However, the cameras and sensors capable of collecting such data are typically installed only on specific roads, resulting in limited spatial coverage. To address this issue, previous studies have proposed methods that utilize dashcam footage to supplement missing data. Many of these approaches estimate object positions based on vanishing point geometry, which makes it challenging to acquire wide-area positional information while maintaining accuracy. In this study, we develop an algorithm that estimates the two-dimensional positions and velocities of surrounding vehicles and pedestrians using object detection and monocular depth estimation from dashcam video. Through accuracy evaluation, we demonstrate the effectiveness of using monocular depth estimation for two-dimensional spatial representation and highlight its potential to capture the locations and movements of vehicles and pedestrians within a localized area.