Application Research on End-To-End Deep Person Re-Identification

Abstract Pedestrian detection refers to the technology of predicting and locating the location of pedestrians in video or image. However, the recognition accuracy of existing re-identification methods needs to be improved. In this paper, the deep learning method is adopted, and the pedestrian detection based on YOLOV3-spp is combined with the pedestrian re-identification based on Resnet50, to construct the deep pedestrian re-identification system. In the training part, the COCO training set containing only pedestrian images is used to train pedestrian detection. WGAN-GP was used to expand the pedestrian re-identification training set of Market1501, and pedestrian re-recognition training was carried out on the expanded dataset to reduce the interference of pose diversity in the learning process. In the detection part, the system first uses the pedestrian detection to locate the original image and cut out the corresponding image, then input the cut image and the target image into the person re-identification network to learn the image features, and finally the classifier judges the image containing the target. In the experiment, the identification accuracy of the system is 87.5% on the Market-1501 test set, and the identification speed of each picture is 0.12s.