Vision-based Vehicle Speed Estimation: A Survey

TL;DR

This survey reviews vision-based vehicle speed estimation techniques, discussing their applications, challenges, datasets, and evaluation metrics, highlighting the potential for cost-effective and accurate traffic monitoring solutions.

Contribution

It provides a comprehensive taxonomy and overview of existing vision-based vehicle speed estimation methods, including performance metrics and datasets, and discusses future research directions.

Findings

Vision-based systems can reduce costs compared to range sensors.

Existing methods vary widely in accuracy and robustness.

Future work should address current limitations and improve reliability.

Abstract

The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision-based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision-based vehicle speed estimation. We describe the terminology, the application domains, and propose a complete taxonomy of a large selection of works that categorizes all stages involved. An overview of performance evaluation metrics and available datasets is provided. Finally, we discuss current limitations and future directions.