Tennis Player Actions Dataset for Human Pose Estimation
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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.
本数据集涵盖网球运动中的4类不同动作，每类动作对应500张图像与一份COCO格式的JSON标注文件。 数据集内的图像均从自行录制的视频中逐帧提取，并根据不同网球动作完成人工分类标注。 本数据集的动作类别（COCO格式下的类别名称标注于括号内）如下： 1. 反手击球（backhand） 2. 正手击球（forehand） 3. 准备姿势（ready_position） 4. 发球（serve） 本数据集设置两个核心目录：标注目录与图像目录： - 标注目录（annotations）：存储各类动作的COCO格式JSON标注文件 - 图像目录（images）：存储各类动作的图像，按4类动作分为4个子文件夹 我们采用COCO标注工具（COCO-Annotator）完成人体动作的标注与分类工作。本次标注的关键点遵循OpenPose的标注规范，具体包括：["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"]。 若需开展网球动作捕捉相关的模型训练，用户可自行进行标注工作。
