A Lightweight Network Based on Pyramid Residual Module for Human Pose Estimation
Bingkun Gao, Ke Ma, Hongbo Bi, Ling Wang · Pattern Recognition and Image Analysis · 2019
Abstract The human pose estimation is one of the most popular research fields. Its current accuracy is satisfactory in some cases, however, there exists a challenge for practical application due to the limited memory and computational efficiency in FPGAs and other hardware. We propose a lightweight module based on the pyramid residual module in this work. We change the convolution mode by using the depth-wise separable convolutions structure. Meanwhile, the channel split module and channel shuffle module are added to change the feature graph dimension. As a result, the parameters of the network are reduced effectively. We test the network on standard benchmarks MPII dataset, our method reduces about 50% of the training storage space while maintaining comparable accuracy. The complexity is simplified from 9 GFLOPs to 3 GFLOPs.