ForeSpeed: A Real-World Video Dataset of CCTV Cameras with Different Settings for Vehicle Speed Estimation

The need to estimate the speed of road vehicles has become increasingly important in the field of video forensics, particularly with the widespread deployment of CCTV cameras worldwide. Despite the development of various approaches, the accuracy of forensic speed estimation from real-world footage remains highly dependent on several factors, including camera specifications, acquisition methods, spatial and temporal resolution, compression methods, and scene perspective, all of which can significantly influence performance. In this paper, we introduce ForeSpeed, a comprehensive dataset designed to support the evaluation of speed estimation techniques in real-world scenarios using CCTV footage. The dataset includes recordings of 14 vehicle passes at known speeds, captured by three digital and three analog cameras from two distinct perspectives. Real-world road metrics are provided to enable the restoration of the scene geometry. Videos were exported with multiple compression levels and settings to simulate real-world scenarios in which export procedures are not always performed according to forensic standards. Overall, ForeSpeed includes a collection of 322 videos. As a case study, we employed the ForeSpeed dataset to benchmark a speed estimation algorithm available in a commercial product (Amped FIVE). The results demonstrate that while the method reliably estimates average speed across various conditions, the coverage rate and the estimated error range can be significantly affected by perspective. Furthermore, having higher spatial or temporal resolution alone does not guarantee better reliability of the estimates. The ForeSpeed dataset is publicly available to the forensic community, with the aim of facilitating the evaluation of current methodologies and inspiring the development of new, robust solutions tailored to collision investigation and forensic incident analysis.