Include50 Full Body Keypoints

Landmark extraction was conducted using the MediaPipe Holistic pipeline, which concurrently tracks the pose, face, and hands. This process generated a total of 543 landmarks per frame, encompassing 33 pose, 468 face, and 21 keypoints per hand. Each landmark is represented by its spatial (x, y, z) coordinates, resulting in a 1,629-dimensional feature vector (543 * 3) per timestep. The processed features were serialized and stored in the NumPy (.npy) file format to facilitate efficient data loading during training. Citation @inproceedings{10.1145/3394171.3413528,author = {Sridhar, Advaith and Ganesan, Rohith Gandhi and Kumar, Pratyush and Khapra, Mitesh},title = {INCLUDE: A Large Scale Dataset for Indian Sign Language Recognition},year = {2020},isbn = {9781450379885},publisher = {Association for Computing Machinery},doi = {10.1145/3394171.3413528},numpages = {10},series = {MM '20}}本研究采用MediaPipe Holistic管线完成关键点提取，该管线可同时追踪人体姿态、面部与手部信息。该流程每帧可生成共计543个关键点，涵盖33个姿态关键点、468个面部关键点以及单只手部的21个关键点。每个关键点以空间(x, y, z)坐标进行表征，因此每一时间步可得到1629维的特征向量（543×3）。经处理后的特征将被序列化并存储为NumPy（.npy）文件格式，以在训练阶段实现高效的数据加载。 ### 引用 @inproceedings{10.1145/3394171.3413528, 作者 = {阿德维思·斯里达尔、罗希思·甘地·加内尚、普拉蒂尤什·库马尔、米特什·卡普拉}, 标题 = {INCLUDE：面向印度手语识别的大规模数据集}, 年份 = {2020}, 国际标准书号 = {9781450379885}, 出版者 = {国际计算机学会}, doi = {10.1145/3394171.3413528}, 总页码 = {10}, 会议系列 = {MM '20}}