TITLE: A dataset for audio-video based vehicle speed estimation 

ABSTRACT: Accurate speed estimation of road vehicles is important for several reasons.
One is speed limit enforcement, which represents a crucial tool in decreasing
traffic accidents and fatalities. Compared with other research areas and
domains, the number of available datasets for vehicle speed estimation is still
very limited. We present a dataset of on-road audio-video recordings of single
vehicles passing by a camera at known speeds, maintained stable by the on-board
cruise control. The dataset contains thirteen vehicles, selected to be as
diverse as possible in terms of manufacturer, production year, engine type,
power and transmission, resulting in a total of $ 400 $ annotated audio-video
recordings. The dataset is fully available and intended as a public benchmark
to facilitate research in audio-video vehicle speed estimation. In addition to
the dataset, we propose a cross-validation strategy which can be used in a
machine learning model for vehicle speed estimation. Two approaches to
training-validation split of the dataset are proposed.
Accurate speed estimation of road vehicles is important for several reasons.
One is speed limit enforcement, which represents a crucial tool in decreasing
traffic accidents and fatalities. Compared with other research areas and
domains, the number of available datasets for vehicle speed estimation is still
very limited. We present a dataset of on-road audio-video recordings of single
vehicles passing by a camera at known speeds, maintained stable by the on-board
cruise control. The dataset contains thirteen vehicles, selected to be as
diverse as possible in terms of manufacturer, production year, engine type,
power and transmission, resulting in a total of $ 400 $ annotated audio-video
recordings. The dataset is fully available and intended as a public benchmark
to facilitate research in audio-video vehicle speed estimation. In addition to
the dataset, we propose a cross-validation strategy which can be used in a
machine learning model for vehicle speed estimation. Two approaches to
training-validation split of the dataset are proposed.
A dataset for audio-video based vehicle speed estimation
http://arxiv.org/abs/2212.01651v1