Training Sample Formation for Convolution Neural Networks to Person Re-Identification from Video

Abstract

To improve the person re-identification system accuracy, an integrated approach is proposed in the formation of a training sample for convolutional neural networks, which involves the use of a new image dataset, an increase in the training examples number using existing datasets, and the use of a number of transformations to increase their diversity. The created dataset PolReID1077 contains images of people that were obtained in all seasons, which will improve the correct operation of re-identification systems when the seasons change. Another PolReID1077 advantage is the video data use obtained from external and internal surveillance in a large number of different filming locations. Therefore, the people images in the created set are characterized by the variability of the background, brightness and color characteristics. Joining the created dataset with the existing CUHK02, CUHK03, Market-1501, DukeMTMC-ReID and MSMT17 sets made it possible to obtain 109 772 images for training. An increase in the variety of generated examples is achieved by applying a cyclic shift to them, eliminating color and replacing a fragment with a reduced copy of another image. The research results on estimating the accuracy of re-identification for the ResNet-50 and DenseNet-121 convolutional neural networks during their training, using the proposed approach to form a training sample, are presented.