  The rapid development of autonomous driving, abnormal behavior detection, and
behavior recognition makes an increasing demand for multi-person pose
estimation-based applications, especially on mobile platforms. However, to
achieve high accuracy, state-of-the-art methods tend to have a large model size
and complex post-processing algorithm, which costs intense computation and long
end-to-end latency. To solve this problem, we propose an architecture
optimization and weight pruning framework to accelerate inference of
multi-person pose estimation on mobile devices. With our optimization
framework, we achieve up to 2.51x faster model inference speed with higher
accuracy compared to representative lightweight multi-person pose estimator.
