JDE algorithm in multiobject tracking jointly learns target detection and reidentification for the first time, which greatly improves the tracking speed.However, the tracking accuracy is reduced due to the poor tracking effect caused by complex background interference and occlusion processing.In order to balance the tracking speed and accuracy, SAMJDE is proposed in this paper. This model integrates SimAM attention mechanism, multiscale fusion and other ideas to improve the accuracy of target tracking by enhancing the ability of feature extraction. CIoU_Loss is used as the regression loss function to improve the positioning accuracy by accurately building the position relationship between the target box and the prediction box.In the association matching part, Kalman filtering is used to predict the motion information, and the Hungarian matching algorithm completes the target association in the time series dimension. Testing on MOT16test dataset shows that MOTA reaches 664% and tracking speed is 206 FPS. On the basis of ensuring realtime performance, tracking accuracy is 23% higher than JDE algorithm, which better optimizes the balance between accuracy and speed.