Tennis Player Actions Dataset for Human Pose Estimation

Published: 30 April 2024| Version 1 | DOI: 10.17632/nv3rpsxhhk.1
Contributors:
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Description

The dataset comprises 4 different actions in tennis, each action has 500 images and a COCO-format JSON files. The images in the dataset were extracted frame by frame from videos that were self-recorded, and manually classified according to different tennis actions. The actions in this dataset, the action categories name in COCO-format is in brackets: 1. backhand shot (backhand) 2. forehand shot (forehand) 3. ready position (ready_position) 4. serve (serve) We organize two main directories: annotations and images. - annotations: the JSON files of the actions (COCO-format) - images: the images of the actions (according four actions classify to four folders) We use COCO-Annotator to annotating and categorizing human actions. And we annotate the key points are in following (refer to OpenPose's annotation): ["nose", "left_eye", "right_eye", "left_ear", "right_ear", "left_shoulder", "right_shoulder", "left_elbow", "right_elbow", "left_wrist", "right_wrist", "left_hip", "right_hip", "left_knee", "right_knee", "left_ankle", "right_ankle", "neck"] If you want to train to capture the tennis, you can annotate yourself.

Files

Steps to reproduce

Download all files, and you can train the model using this dataset or write some algorithm to recognize action using the images. The dataset comprises 4 different actions in tennis, each action have 500 images and a COCO-format JSON files. Size on disk is 508 MB (533,372,928 bytes).

Institutions

National Taichung University of Science and Technology, National Kaohsiung University of Science and Technology

Categories

Computer Vision, Image Processing, Tennis, Action Recognition

Licence