SoccerNet A Scalable Dataset for Event Spotting in Soccer Videos In this paper, we introduce SoccerNet, a benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours.
Soccer Video and Player Position Dataset. Randomized sensor tags: The players' identity in the body-sensor data is randomized to protect the privacy of the players. Although the dataset can be used for general object and people tracking, attempts to re-identify individual players, create player/club performance profiles, and similar, are not allowed.
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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.
In its current version, the dataset contains 222 broadcast soccer videos, totaling 170 video hours. The dataset covers three annotation types: (1) A shot boundary with two shot transition types, and shot type annotations with five shot types; (2) Event annotations with 11 event labels, and a story annotation with 15 story labels at coarser granularity; and (3) bounding boxes of the players under analysis in a subset of 19908 video frames.
ABSTRACT. This paper presents a dataset of body-sensor traces and corresponding videos from several professional soccer games captured in late 2013 at the Alfheim Stadium in Tromsø, Norway. Player data, including field position, heading, and speed are sampled at 20Hz using the highly accurate ZXY Sport Tracking system.
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Silvio Giancola, Mohieddine Amine, Tarek Dghaily, Bernard Ghanem"SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos" IEEE Conference on Compu...
SoccerDB. Comprises of 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks.
Therefore, we present a highly unconstrained dataset of sports videos, called Sport Videos in the Wild (SVW). SVW is comprised of 4200 videos captured solely with smartphones by users of Coach’s Eye smartphone app, a leading app for sports training developed by TechSmith corporation. SVW includes 30 categories of sports and 44 different actions.