Sports Ball Detection and Tracking

Volleyball is a video action recognition dataset. It has 4830 annotated frames that were handpicked from 55 videos with 9 player action labels and 8 team activity labels. It contains group activity annotations as well as individual activity annotations. Source: https://github.com/mostafa-saad/deep-activity-rec#dataset Image Source: https://github.com/mostafa-saad/deep-activity-rec#dataset

• This dataset was originally introduced by [1] for soccer ball and player tracking from six synchronized videos. - Since ball annotations provided by [1] are collapsed, new annotations of ball 2D coordinates are provided by [2] - For sports ball detection and tracking evaluation, the first four video clips are used for training and the remaining two clips are for testing. [1] T. D’Orazio et al., A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences, in AVSS, 2009. [2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, in BMVC, 2023.

This dataset was introduced by [1], but was not used in its experiment. [2] propose to use all the clips included in the first 7 games as a training set, and the remainings as a testing set. [1] Y.-C. Huang et al., TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications. AVSS, 2019. [2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, BMVC, 2023.

This dataset was introduced by the TrackNetV2 work. Following the dataset split defined by the authors, we use all the clips from 26 matches as a training set and the remaining 3 matches as a testing set.

TBD