Citation

Tan DM, Kieu LM. IATSS Res. 2023; 47(4): 468-481.

Copyright

(Copyright © 2023, International Association of Traffic and Safety Sciences, Publisher Elsevier Publishing)

DOI

10.1016/j.iatssr.2023.10.001

PMID

unavailable

Abstract

This paper introduces a new visual dataset and framework to facilitate computer-vision-based traffic monitoring in high density, mixed and lane-free traffic (TRAMON). While there are advanced deep learning algorithms that can detect and track vehicles from traffic videos, none of the existing systems provides accurate traffic monitoring in mixed traffic. The mixed traffic flows in developing countries often includes the types of vehicles that are not widely known by the existing visual datasets. The computer vision algorithms also face difficulties in detecting and tracking a high density of vehicles that are not following lanes. This paper proposes a large-scale visual dataset of >282,000 labelled images of traffic vehicles, as well as a comprehensive framework and strategy to train common deep-learning-based computer vision algorithms to detect and track vehicles in high density, heterogeneous and lane-free traffic. A systematic evaluation of results shows that TRAMON, the proposed visual dataset and framework, performs well and better than the common visual dataset at all traffic densities.

Language: en

Keywords

Deep learning; Mixed traffic; Traffic data collection; Traffic monitoring