A dataset for audio-video based vehicle speed estimation
This provides a new dataset for researchers in audio-video vehicle speed estimation, which is incremental as it addresses a data gap rather than introducing novel methods.
The authors tackled the limited availability of datasets for vehicle speed estimation by creating a public benchmark of 400 annotated audio-video recordings of 13 diverse vehicles at known speeds, intended to facilitate research in this area.
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.