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Object Tracking Benchmark (OTB) is a visual tracking benchmark that is widely used to evaluate the performance of a visual tracking algorithm. The dataset contains a total of 100 sequences and each is annotated frame-by-frame with bounding boxes and 11 challenge attributes. OTB-2013 dataset contains 51 sequences and the OTB-2015 dataset contains all 100 sequences of the OTB dataset. Source: Deep Meta Learning for Real-Time Target-Aware Visual Tracking

OTB-2015, also referred as Visual Tracker Benchmark, is a visual tracking dataset. It contains 100 commonly used video sequences for evaluating visual tracking. Image Source: http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html

OTB2013 is the previous version of the current OTB2015 Visual Tracker Benchmark. It contains only 50 tracking sequences, as opposed to the 100 sequences in the current version of the benchmark. Source: Marvasti-Zadeh

The football keyword dataset (FKD), as a new keyword spotting dataset in Persian, is collected with crowdsourcing. This dataset contains nearly 31000 samples in 18 classes.

KTI Multiview Football II consists of images of professional footballers during a match of the Allsvenskan league. It consists of two parts: one with ground truth pose in 2D and one with ground truth pose in both 2D and 3D. The 3D dataset has 800 time frames, captured from 3 views (2400 images). Views are calibrated and synchronized. 3D ground truth pose and orthographic camera matrices are provided for each frame. There are 14 annotated joints. Lastly, there are 2 different players and two sequences per player. Source: http://www.csc.kth.se/~vahidk/football_data.html Image Source: http://www.csc.kth.se/cvap/cvg/?page=footballdataset2

Google Research Footbal is a new reinforcement learning environment where agents are trained to play football in an advanced, physics-based 3D simulator. The resulting environment is challenging, easy to use and customize, and it is available under a permissive open-source license. In addition, it provides support for multiplayer and multi-agent experiments. Source: Google Research Football: A Novel Reinforcement Learning Environment

KTI Multiview Football I is a dataset of football players with annotated joints that can be used for multi-view reconstruction. The dataset includes 771 images of football players, images taken from 3 views at 257 time instances, and 14 annotated body joints. Source: http://www.csc.kth.se/~vahidk/football_data.html Image Source: http://www.csc.kth.se/~vahidk/football_data.html

academy 3 vs 1 with keeper on Google Research Football