Given that the existing multi-target tracking algorithm cannot track accurately after occlusion, a multi-target tracking algorithm using the improved attention mechanism and Kalman filter is proposed. The structure of joint detection and embedding is used to extract features and accomplish object detection and identification simultaneously. A parallel-structured attention mechanism is proposed, containing both spatial and channel parts. Each part is designed into parallel branches for pooling and convolution. During tracking, the proposed velocity-prediction Kalman filter is adopted for the more accurate prediction of pedestrian movements. The CUHK-SYSU dataset is used for training, and the algorithm is verified and tested on the MOT16 dataset. The proposed algorithm can achieve 65.1% MOTA, 78.8% MOTP, and 62.3% IDF1. The experimental results show that the proposed tracking algorithm can improve the overall tracking performance and achieve continuous tracking.