  Detection and tracking of vehicles captured by traffic surveillance cameras
is a key component of intelligent transportation systems. We present an
improved version of our algorithm for detection of 3D bounding boxes of
vehicles, their tracking and subsequent speed estimation. Our algorithm
utilizes the known geometry of vanishing points in the surveilled scene to
construct a perspective transformation. The transformation enables an intuitive
simplification of the problem of detecting 3D bounding boxes to detection of 2D
bounding boxes with one additional parameter using a standard 2D object
detector. Main contribution of this paper is an improved construction of the
perspective transformation which is more robust and fully automatic and an
extended experimental evaluation of speed estimation. We test our algorithm on
the speed estimation task of the BrnoCompSpeed dataset. We evaluate our
approach with different configurations to gauge the relationship between
accuracy and computational costs and benefits of 3D bounding box detection over
2D detection. All of the tested configurations run in real-time and are fully
automatic. Compared to other published state-of-the-art fully automatic results
our algorithm reduces the mean absolute speed measurement error by 32% (1.10
km/h to 0.75 km/h) and the absolute median error by 40% (0.97 km/h to 0.58
km/h).
