To address the low feasibility of human pose estimation algorithms and low accuracy of jump rope counting based on pose estimation, this study proposes a jump rope counting algorithm based on a lightweight human pose estimation network. The algorithm first inputs a jump rope video, then extracts keyframe images by inter-frame difference method, and feeds them into the human pose estimation network for key joint point detection. To improve the detection accuracy of the lightweight network, the study builds an optimized LitePose detection model, which employs adaptive perception decoding to optimize the decoding part in the model and reduce quantization errors. Furthermore, a Kalman filter is adopted to smooth and denoise the coordinate data, reducing coordinate jitter errors. Finally, jump rope counting is determined based on the changes in key-point coordinates. Experimental results demonstrate that, in the same image resolution and environmental conditions, the proposed algorithm employing the optimized LitePose-S network model does not increase the parameter size and computational complexity of the model but improves network detection accuracy by 0.7% compared with other comparison networks. Meanwhile, the average error rate of this algorithm in jump rope counting can reach a minimum of 1.00%. The algorithm effectively determines the takeoff and landing of the human body by the results of human pose estimation and yields counting results.