ForeSpeed: A real-world video dataset of CCTV cameras with different settings for vehicle speed estimation

TL;DR

ForeSpeed is a comprehensive real-world CCTV vehicle speed dataset designed to evaluate and improve forensic speed estimation methods under diverse conditions, including different camera settings and compression artifacts.

Contribution

This paper introduces ForeSpeed, a new publicly available dataset with real-world CCTV footage and known vehicle speeds, enabling more accurate evaluation of speed estimation techniques.

Findings

Speed estimation accuracy varies with scene perspective and compression.

The commercial speed estimation method reliably estimates average speed.

Uncertainty increases with perspective distortion.

Abstract

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, 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 a vehicle traveling 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 stored with multiple compression factors 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). Results demonstrate that while the method reliably estimates average speed across various conditions, its uncertainty range significantly increases when the scene involves strong perspective distortion. 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.