  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.
