Human pose estimation (HPE) is a classical task in computer vision that focuses on representing the orientation of a person by identifying the positions of their joints. We design a lighterversion of the stacked hourglass network with minimal loss in performance of the model. The lightweight 2-stacked hourglass has a reduced number of channels with depthwise separable convolutions, residual connections with concatenation, and residual connections between the necks of the hourglasses. The final model has a marginal drop in performance with 79% reduction in the number of parameters and a similar drop in MAdds
@article{arxiv.2302.04815,
title = {To Perceive or Not to Perceive: Lightweight Stacked Hourglass Network},
author = {Jameel Hassan Abdul Samadh and Salwa K. Al Khatib},
journal= {arXiv preprint arXiv:2302.04815},
year = {2023}
}