SoccerNet Camera Calibration
Camera calibration is the link between the image world and the 3D real world. Automatic calibration of the camera is an important topic of research for sports analytics that can lead to interesting applications such as offline line analysis. It is also the key to integrate reality graphics into any live production. We defined two tasks on this topic: soccer pitch marking and goal post localization, and the automatic camera calibration (this task), with the results of the first task that can be used as inputs to the second.
Automatic camera calibration
Given a common 3D pitch template, the camera parameters are used to estimate the reprojection error induced by the camera parameters. The camera parameters include its lens parameters, its orientation, its translation with respect to the world reference axis system that we define accordingly.
The data consists of the same 20,028 images as for the first task, so taken from the 500 games of soccernet at events annotated for the action spotting task, and images from their replays. Likewise, The challenge set is composed of the same 2,104 images.
The evaluation is based on the reprojection error which we define here as the L2 distance between one annotated point and the line to which the point belong. We consider a pitch marking to be one entity, and for it to be correctly detected, all its extremities (or all points annotated for circles) must have a reprojection error smaller than the threshold. We also measure the completeness rate as the number of camera parameters provided divided by the number of images with more than four semantic line annotations in the dataset.
2023 challenge leaderboard
2022 challenge leaderboard
Our videos on Calibration
Soccer Player Tracking, Re-ID, Camera Calibration and Action Spotting - SoccerNet Challenges 2022
In this video, we present our new SoccerNet Challenges for CVPR 2022! We introduce the three tasks of Calibration, Re-identification and Tracking on soccer games, in partnership with EVS Broadcast Equipment, SportRadar and Baidu Research. We also reiterate our previous Action Spotting and Replay Grounding Challenges at the ActivityNet workshop.