Optimized Video Tracking for Automated Vehicle Turning Movement Counts

Author:

Bélisle François1,Saunier Nicolas2,Bilodeau Guillaume-Alexandre3,le Digabel Sebastien4

Affiliation:

1. MathMobile.io, 17 West Gouin Boulevard, Montreal, Quebec H3L 1H9, Canada

2. Department of Civil, Geological, and Mining Engineering, Polytechnique Montréal, CP 6079, Succursale Centre-Ville, Montreal, Quebec H3C 3A7, Canada

3. Department of Computer and Software Engineering, Polytechnique Montréal, CP 6079, Succursale Centre-Ville, Montreal, Quebec H3C 3A7, Canada

4. Department of Mathematical and Industrial Engineering, Polytechnique Montréal, CP 6079, Succursale Centre-Ville, Montreal, Quebec H3C 3A7, Canada

Abstract

This paper proposes a new method for automatically counting vehicle turning movements based on video tracking, expanding on previous work on optimization of parameters for road user trajectory extraction and on automated trajectory clustering. The counting method is composed of three main steps: an automated tracker that extracts vehicle trajectories from video data, an automated trajectory clustering algorithm, and an optimization algorithm. The proposed method was applied to obtain turning movement counts in three typical traffic engineering case studies in Canada representing industry-type conditions. These exhibited varying levels of tracking difficulty, ranging from a single-lane off-ramp to a six-movement intersection with a stop and a right-turn channel. Because of a limitation of the data set, giving flows per movement and not per lane, all sites were chosen with a single lane per movement. The 3-h morning peak period was used in the case studies. The results show an average weighted generalization error of 12% for more than 3,700 vehicles automatically analyzed for more than 8 h of video, ranging from 9.5% to 19.5%. The generalization error is on average 8.6% (and as low as 6.0% per movement) for the 3,084 uninterrupted vehicles that are in plain view of the camera. This paper describes in detail the methodology used and discusses the factors that affect counting performance and how to improve counting accuracy in further research.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Development of a Turning Movement Estimator Using CV Data;Future Transportation;2023-03-03

2. Study of automated shuttle interactions in city traffic using surrogate measures of safety;Transportation Research Part C: Emerging Technologies;2022-02

3. VIFECO: An Open-Source Software for Counting Features on a Video;Journal of Open Research Software;2021

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