Tennis Player Actions Dataset for Human Pose Estimation

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.