  Estimating the speed of vehicles using traffic cameras is a crucial task for
traffic surveillance and management, enabling more optimal traffic flow,
improved road safety, and lower environmental impact. Transportation-dependent
systems, such as for navigation and logistics, have great potential to benefit
from reliable speed estimation. While there is prior research in this area
reporting competitive accuracy levels, their solutions lack reproducibility and
robustness across different datasets. To address this, we provide a novel
framework for automatic real-time vehicle speed calculation, which copes with
more diverse data from publicly available traffic cameras to achieve greater
robustness. Our model employs novel techniques to estimate the length of road
segments via depth map prediction. Additionally, our framework is capable of
handling realistic conditions such as camera movements and different video
stream inputs automatically. We compare our model to three well-known models in
the field using their benchmark datasets. While our model does not set a new
state of the art regarding prediction performance, the results are competitive
on realistic CCTV videos. At the same time, our end-to-end pipeline offers more
consistent results, an easier implementation, and better compatibility. Its
modular structure facilitates reproducibility and future improvements.
