Automated Construction of Time-Space Diagrams for Traffic Analysis Using Street-View Video Sequence

TL;DR

This paper presents a novel method for constructing time-space diagrams from street-view videos captured by moving vehicles, leveraging advanced computer vision techniques to analyze traffic patterns and improve transportation planning.

Contribution

The study introduces an innovative approach combining YOLOv5, StrongSORT, and photogrammetry to generate vehicle trajectories from street-view videos, enhancing traffic analysis capabilities.

Findings

Successfully generated vehicle trajectories from video data

Demonstrated potential for comprehensive traffic pattern analysis

Identified areas for improving detection and tracking accuracy

Abstract

Time-space diagrams are essential tools for analyzing traffic patterns and optimizing transportation infrastructure and traffic management strategies. Traditional data collection methods for these diagrams have limitations in terms of temporal and spatial coverage. Recent advancements in camera technology have overcome these limitations and provided extensive urban data. In this study, we propose an innovative approach to constructing time-space diagrams by utilizing street-view video sequences captured by cameras mounted on moving vehicles. Using the state-of-the-art YOLOv5, StrongSORT, and photogrammetry techniques for distance calculation, we can infer vehicle trajectories from the video data and generate time-space diagrams. To evaluate the effectiveness of our proposed method, we utilized datasets from the KITTI computer vision benchmark suite. The evaluation results demonstrate that our approach can generate trajectories from video data, although there are some errors that can be mitigated by improving the performance of the detector, tracker, and distance calculation components. In conclusion, the utilization of street-view video sequences captured by cameras mounted on moving vehicles, combined with state-of-the-art computer vision techniques, has immense potential for constructing comprehensive time-space diagrams. These diagrams offer valuable insights into traffic patterns and contribute to the design of transportation infrastructure and traffic management strategies.