Citation

Huang X, He P, Rangarajan A, Ranka S. J. Imaging 2022; 8(4): e101.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/jimaging8040101

PMID

35448228

Abstract

Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method.

Language: en

Keywords

deep learning; intelligent transportation systems; travel-time computation; vehicle signature