Vehicle counting and traffic flow parameter estimation for dense traffic scenes. Issue 12 (1st December 2020)

• Record Type: Journal Article
• Authors: Li, Shuang; Chang, Faliang; Liu, Chunsheng; Li, Nanjun
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 12(2020)
• Page Start: 1517
• Page End: 1523
• Publication Date: 2020-12-01
• Subjects: parameter estimation -- computer vision -- object detection -- road vehicles -- road traffic -- traffic engineering computing; vehicle counting -- dense traffic scenes -- vision‐based traffic flow parameter estimation -- dense traffic etc -- nondense traffic scenes -- estimate traffic flow parameters -- main traffic flow parameters -- different traffic flow parameters; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0521
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : The vision‐based traffic flow parameter estimation is a challenging problem especially for dense traffic scenes, due to the difficulties of occlusion, small‐size and dense traffic etc. Yet, previous methods mainly use detection and tracking methods to do vehicle counting in non‐dense traffic scenes and few of them further estimate traffic flow parameters in dense traffic scenes. A framework is proposed to count vehicles and estimate traffic flow parameters in dense traffic scenes. First, a pyramid‐YOLO network is proposed for detecting vehicles in dense scenes, which can effectively detect small‐size and occluded vehicles. Second, the authors design a line of interest counting method based on restricted multi‐tracking, which counts vehicles crossing a counting line at a certain time duration. The proposed tracking method tracks short‐term vehicle trajectories near the counting line and analyses the trajectories, thus improving tracking and counting accuracy. Third, based on the detection and counting results, an estimation model is proposed to estimate traffic flow parameters of volume, speed and density. The evaluation experiments on the databases with dense traffic scenes show that the proposed framework can efficiently count vehicles and estimate traffic flow parameters with high accuracy and outperforms the representative estimation methods in comparison.