Aiming at the difficulty of trajectory tracking of helmet wearers caused by target occlusion in construction sites, a helmet wearing detection and trajectory tracking method combining YOLO v5 and centroid matching algorithm is proposed in this paper. Firstly, YOLO v5 network is used to precisely detect the personnel who do not wear safety helmets and calculate their centroid coordinates. Further, the extended Kalman filter is used to predict the target position information. Finally, the centroid matching association algorithm based on Mahalanobis distance and histogram correlation is adopted. Combined with the prediction information, the target trajectory anomaly correction in the target occlusion environment is realized, and the accurate target trajectory can be obtained. The experimental results show that the proposed method effectively solves the problems of target exchange and loss caused by target occlusion in target tracking, and obtains more than 10% target tracking accuracy higher than the traditional algorithm in the self-built data set, It provides strong technical support for the development of smart construction sites.