LWIRPOSE: A novel LWIR Thermal Image Dataset and Benchmark
This provides a new dataset for researchers and practitioners to improve pose estimation in scenarios with occlusions and lighting changes, but it is incremental as it focuses on data rather than method innovation.
The authors tackled the problem of human pose estimation in challenging real-world conditions by introducing a new LWIR thermal image dataset with over 2,400 annotated images, establishing a benchmark that shows its effectiveness for applications like surveillance and healthcare.
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 https://github.com/avinres/LWIRPOSE