Fusion-Based Human Pose Estimation Using RGB and IR Images with Transformer-Based Decoding

In this paper, we present ongoing research on a method for human pose estimation under varying lighting conditions, with enhanced occlusion handling, real-time efficiency, and improved association accuracy. Our approach addresses a significant limitation of conventional pose estimation methods, which typically treat the tasks of keypoints localization and body joint association separately. By integrating these tasks through transformer-based architectures, we simultaneously localize and associate keypoints, leading to superior performance in complex environments. Features are encoded and decoded within this framework, showing considerable improvements in managing occlusions. This research is in its preliminary stages, and we are continuing to refine and enhance the method, with plans to publish further results in the future.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>