LWIRPOSE: A novel LWIR Thermal Image Dataset and Benchmark

TL;DR

This paper introduces LWIRPOSE, a new thermal image dataset with annotated human poses, enabling improved pose estimation in challenging conditions like occlusion and low lighting, and benchmarks current methods on this dataset.

Contribution

The paper presents a novel LWIR thermal image dataset with detailed annotations, filling a gap for pose estimation in thermal imaging and providing a benchmark for future research.

Findings

State-of-the-art methods perform variably on the dataset

The dataset improves pose estimation in occlusion scenarios

Baseline results establish a foundation for future work

Abstract

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