Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement
This provides a standardized benchmark for researchers in visual traffic surveillance, addressing a key bottleneck in method comparison, though it is incremental as it builds on existing calibration techniques.
The authors tackled the lack of a common dataset for comparing visual speed measurement methods from single monocular cameras by creating a new dataset of 18 full-HD videos with 20,865 vehicle instances annotated with precise speed measurements from LiDAR and GPS. They also analyzed an existing automatic calibration method on this dataset, reporting detailed results.
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.