  In this study, we present a pragmatic lightweight pose estimation model. Our
model can achieve real-time predictions using low-power embedded devices. This
system was found to be very accurate and achieved a 94.5% accuracy of SOTA
HRNet 256x192 using a computational cost of only 3.8% on COCO test dataset. Our
model adopts an encoder-decoder architecture and is carefully downsized to
improve its efficiency. We especially focused on optimizing the deconvolution
layers and observed that the channel reduction of the deconvolution layers
contributes significantly to reducing computational resource consumption
without degrading the accuracy of this system. We also incorporated recent
model agnostic techniques such as DarkPose and distillation training to
maximize the efficiency of our model. Furthermore, we applied model
quantization to exploit multi/mixed precision features. Our FP16'ed model (COCO
AP 70.0) operates at ~60-fps on NVIDIA Jetson AGX Xavier and ~200 fps on NVIDIA
Quadro RTX6000.
