The player tracking system provides the player coordinates on the field, their speed, acceleration and force together with an ID and timestamp. The camera array covers the entire field, and each camera can be used individually or as a stitched panorama video. In addition to the obvious sport analytics scenario, the dataset can be used several ways.
Dataset of elite soccer player movements and corresponding videos. The dataset is captured at Alfheim Stadium – the home arena for Tromsø IL (Norway). The player postions are measured at 20 Hz using the ZXY Sport Tracking system, and the video is captured from the middle of the field using two camera arrays.
Player tracking on video and bird’s-eye view. With player’s movement information, it is possible to do further analysis such as players’ running distance and velocity. The speed for running this player tracking is around 0.3 second per frame on my 2016 Macbook Pro Intel i5 CPU.
Use our datasets to train other models; Finetune some of our trained models; Use our trackers; Evaluate players with our EDG Agent; and much more; Narya. The Narya API allows you to track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository contains the implementation of the following paper. We also make available all of our trained agents, and the datasets we used as well.
This dataset contains four 30-seconds video sequences of eight people playing soccer in an indoor arena (court size 4020 metres). The video is captured by thermal cameras of type AXIS Q1922 with a resolution of 640480 pixels and 25 fps. The three images are stitched to one image of 1920*480 pixels. The videos are manually annotated for tracking.
Football players tracking. Color of the bounding box represents the color of jersey. Then similar to my previous post, by using Opencv’s getPerspectiveTransform, I obtained the bird’s-eye view as shown in the beginning.
Soccer-logs can be used to compare the performance of players and track their evolution in time. As an example, we compare three forwards with different characteristics – L. Messi (FC Barcelona ...
Hey guys. Are there any datasets that have been made by body sensors on players to get attributes like. Player position. PLayer speed. Ball position. Ball speed etc. If yes, please help with links. Thanks!
soccermatics provides tools to visualise spatial tracking and event data from football (soccer) matches. There are currently functions to visualise shot maps (with xG), average positions, heatmaps, and individual player trajectories. There are also helper functions to smooth, interpolate, and prepare x,y-coordinate tracking data for plotting ...