Abstract: Aimed at the problems of difficult feature extraction and poor real-time performance in traffic police pose estimation, an improved traffic police pose estimation algorithm based on OpenPose network is proposed. MobileNet was used as the backbone network to extract the features of traffic police pose, which solved the problem of network degradation caused by the deepening of the model layers, reduced the number of network parameters and speeded up the calculation of internal features. The parallel structure of the model was improved to the series-parallel structure by the jump connection mechanism, which could realize the parameter sharing within the network, reduce the complexity of the model and improve the real-time performance of detection. The experimental results show that the improved model can obtain 78.9% and 74.9% MAP on COCO dataset and open traffic police dataset, respectively, and the detection speed can reach 25 FPS. It provides a robust and real-time method for traffic police pose estimation.