LFSimCC: Spatial fusion lightweight network for human pose estimation

To address the limitations of existing 2D human pose estimation methods in terms of speed and lightweight, we propose a method called Lightweight Fusion SimCC (LFSimCC). LFSimCC incorporates two modules: LiteFNet, which enhances multi-scale spatial information fusion, and LKC-GAU, which improves the modeling capability of spatial information. Specifically, LiteFNet utilizes a combination of self-attention mechanism and novel spatial convolution to enable feature maps to capture richer multi-level global feature representations within the network. On the other hand, LKC-GAU enhances SimCC’s ability to capture spatial relationships between joints by incorporating a large kernel of convolution and a self-attention mechanism. Furthermore, we design a keypoint information fusion loss (IFL) that enhances the model’s sensitivity to information between keypoints in the human body. Experimental results demonstrate that our method is capable of extracting more decisive information and suppressing redundant feature representations, leading to high recognition accuracy and low inference latency.