Detection Algorithm of Pedestrian Shoe Area Based on Improved YOLOv4

One of the important tactics used by the public security bureau in a criminal investigation is to combine the related surveillance video and shoeprints on the spot to identify criminal suspects. However, the low level automation of such a method is so labor-intensive and time-consuming, which limits its application. Therefore, this paper proposes an object detection method based on the YOLOv4 algorithm to realize the automatic detection of pedestrian shoes in surveillance video. According to the characteristics of the pedestrian shoe area, first, the K-means clustering algorithm is used to determine the scale of the anchor box and confirm its quantity; second, an appropriate detection layer was selected based on the datasets in this paper to improve the learning of shoe features; finally, a multifeature fusion method is used and the adjusted spatial pyramid pooling structure is transferred into the pruned network to improve the learning ability of the model. The experimental results demonstrate that the training weight of the YOLOv4_shoe algorithm proposed is only 39. 56 MB, which is approximately one-sixth of the original model; and its mean average precision reaches 97. 93%, which is 2. 07% higher than that of the original YOLOv4 model.