UULPN: An ultra-lightweight network for human pose estimation based on unbiased data processing

Abstract

Most top-performed human pose estimation methods tend to have a high computational load, which is difficult to transform to resource-limited platforms. To conquer this issue, we propose an ultra-lightweight human pose estimation method based on unbiased data processing called UULPN. Firstly, we design a lightweight bottleneck block with a re-parameterized structure. Through simple linear operations, it generates a large number of feature maps and increases the diversity of feature maps. Secondly, we introduce a multi-branch structure and a single-branch structure in the bottleneck block. In the training phase, a multi-branch structure is adopted to increase the prediction accuracy. In the deploying phase, a single-branch structure is used to improve the model inference speed. These two structures realize the decoupling of the training phase and the deployment phase through the reparameterization technology. In the case of decreased computational cost, they have increased the predicted accuracy. Finally, we present a novel unbiased data processing method to solve quantization errors, which are introduced in the process of image encoding and decoding. Extensive experiment results on the MPII and COCO pose estimation benchmarks indicate that UULPN achieves almost equivalent results with the state-of-the-art methods with less computational cost. In particular, the computational cost of UULPN is almost 31% of HRNet, and the estimated accuracy on the COCO val2017 dataset is up to 74.1%, which is almost the same as HRNet-W32 at the resolution of 256 × 192. It shows that the research further develops in depth, which is of great significance. The code and the proposed method are available on https://github.com/Johnren1111/UULPN.