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We present MovePose, an optimized lightweight convolutional neural networkdesigned specifically for real-time body pose estimation on CPU-based mobiledevices. The current solutions do not provide satisfactory accuracy and speedfor human posture estimation, and MovePose addresses this gap. It aims tomaintain real-time performance while improving the accuracy of human postureestimation for mobile devices. The network produces 17 keypoints for eachindividual at a rate exceeding 11 frames per second, making it suitable forreal-time applications such as fitness tracking, sign language interpretation,and advanced mobile human posture estimation. Our MovePose algorithm hasattained an Mean Average Precision (mAP) score of 67.7 on the COCO\cite{cocodata} validation dataset. The MovePose algorithm displayed efficiencywith a performance of 69+ frames per second (fps) when run on an Inteli9-10920x CPU. Additionally, it showcased an increased performance of 452+ fpson an NVIDIA RTX3090 GPU. On an Android phone equipped with a Snapdragon 8 + 4Gprocessor, the fps reached above 11. To enhance accuracy, we incorporated threetechniques: deconvolution, large kernel convolution, and coordinateclassification methods. Compared to basic upsampling, deconvolution istrainable, improves model capacity, and enhances the receptive field. Largekernel convolution strengthens these properties at a decreased computationalcost. In summary, MovePose provides high accuracy and real-time performance,marking it a potential tool for a variety of applications, including thosefocused on mobile-side human posture estimation. The code and models for thisalgorithm will be made publicly accessible.

MovePose: A High-performance Human Pose Estimation Algorithm on Mobile and Edge Devices