Human pose estimation faces hurdles in real-world applications due to factors
like lighting changes, occlusions, and cluttered environments. We introduce a
unique RGB-Thermal Nearly Paired and Annotated 2D Pose Dataset, comprising over
2,400 high-quality LWIR (thermal) images. Each image is meticulously annotated
with 2D human poses, offering a valuable resource for researchers and
practitioners. This dataset, captured from seven actors performing diverse
everyday activities like sitting, eating, and walking, facilitates pose
estimation on occlusion and other challenging scenarios. We benchmark
state-of-the-art pose estimation methods on the dataset to showcase its
potential, establishing a strong baseline for future research. Our results
demonstrate the dataset's effectiveness in promoting advancements in pose
estimation for various applications, including surveillance, healthcare, and
sports analytics. The dataset and code are available at
this https URL

通过建立一个拥有 2,400 张高质量 LWIR（热力红外）图像的独特 RGB - 热力几乎配对和注释的 2D 动作数据集，该研究论文介绍了一个有助于遮挡和其他挑战情景中动作估计的数据集，并对数据集上的先进动作估计方法进行了基准测试，展示了该数据集在促进动作估计方法发展中的潜力。