# Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement

## TL;DR

This paper introduces a new, comprehensive dataset of 18 full-HD traffic videos with precise vehicle speed annotations for evaluating and comparing visual speed measurement methods from a single monocular camera.

## Contribution

It provides a publicly available dataset with detailed annotations and evaluates a recent automatic camera calibration method for vehicle speed measurement.

## Key findings

- The dataset includes 20,865 vehicle instances with verified ground truth speeds.
- Automatic calibration method's performance is analyzed in detail.
- The dataset enables standardized comparison of visual speed measurement techniques.

## Abstract

In this paper, we focus on traffic camera calibration and a visual speed measurement from a single monocular camera, which is an important task of visual traffic surveillance. Existing methods addressing this problem are difficult to compare due to a lack of a common data set with reliable ground truth. Therefore, it is not clear how the methods compare in various aspects and what factors are affecting their performance. We captured a new data set of 18 full-HD videos, each around 1 hr long, captured at six different locations. Vehicles in the videos (20865 instances in total) are annotated with the precise speed measurements from optical gates using LiDAR and verified with several reference GPS tracks. We made the data set available for download and it contains the videos and metadata (calibration, lengths of features in image, annotations, and so on) for future comparison and evaluation. Camera calibration is the most crucial part of the speed measurement; therefore, we provide a brief overview of the methods and analyze a recently published method for fully automatic camera calibration and vehicle speed measurement and report the results on this data set in detail.