Aiming at the problems of low accuracy and slow speed when only relying on traditional object detection and tracking algorithms in complex scenes， a pedestrian detection and tracking algorithm based on GhostNet and attention mechanism is proposed. First， the backbone network of YOLOv3 is replaced with GhostNet to retain the multi-scale prediction part， the Ghost module is used to reduce the parameters and calculations of the deep network model， and the attention mechanism is integrated into the Ghost module to give higher weight to important features. Then， the direct evaluation index GIoU of object detection is introduced to guide the regression task. Finally， the Deep-Sort algorithm is used for tracking. Experiments on public data sets show that： The mean Average precision （mAP） of the improved model reaches 92.53%， and the frame rate is 2.5 times that of the YOLOv3 model； The tracking accuracy of the proposed algorithm is better than that before the improvement and that of other algorithms； The algorithm can track multi-object pedestrians in complex scenes accurately and effectively， and has strong robustness.