Football match scenes are featured with dense crowds and many mobile targets, and YOLOv3 algorithm has low detection accuracy and requires massive model parameters, which makes it unable to be deployed on mobile devices with limited computing power. In view of these problems, this study proposes a pedestrian detection method based on improved YOLOv3. Specifically, the study replaces the Darknet-53 backbone feature extraction network with a more efficient and lightweight GhostNet network, selects detection branch layers with four scales, and adopts the K-means++ algorithm to improve the clustering effect of the anchor box. Furthermore, the study adds spatial pyramid pooling to achieve an output with the same size as the input image, puts forward the CIoU loss function to calculate the loss value of target positioning, adds heatmap visualization, and uses Mosaic data enhancement in training. The experimental results show that YOLOv3-GhostNet achieves a mAP of 90.97% on the VOC fusion dataset, with an improvement of 1.75% compared with the YOLOv3 algorithm. In addition, it reduces the number of parameters by about 81.4% and increases the real-time detection rate by about 1.5 times, which shows a positive detection effect on small mobile devices.