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In this study, we present a pragmatic lightweight pose estimation model. Ourmodel can achieve real-time predictions using low-power embedded devices. Thissystem was found to be very accurate and achieved a 94.5% accuracy of SOTAHRNet 256x192 using a computational cost of only 3.8% on COCO test dataset. Ourmodel adopts an encoder-decoder architecture and is carefully downsized toimprove its efficiency. We especially focused on optimizing the deconvolutionlayers and observed that the channel reduction of the deconvolution layerscontributes significantly to reducing computational resource consumptionwithout degrading the accuracy of this system. We also incorporated recentmodel agnostic techniques such as DarkPose and distillation training tomaximize the efficiency of our model. Furthermore, we applied modelquantization to exploit multi/mixed precision features. Our FP16'ed model (COCOAP 70.0) operates at ~60-fps on NVIDIA Jetson AGX Xavier and ~200 fps on NVIDIAQuadro RTX6000.

Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing