bsuirДоклады БГУИРDoklady BGUIR1729-76482708-0382БГУИР10.35596/1729-7648-2023-21-3-87-95bsuir-3651Research ArticleЭЛЕКТРОНИКА, РАДИОФИЗИКА, РАДИОТЕХНИКА, ИНФОРМАТИКАELECTRONICS, RADIOPHYSICS, RADIOENGINEERING, INFORMATICSФормирование обучающей выборки для свёрточных нейронных сетей при реидентификации людей по видеоданнымTraining Sample Formation for Convolution Neural Networks to Person Re-Identification from VideoИгнатьеваС. А.IhnatsyevaS. A.

Игнатьева Светлана Александровна,

211440, г. Новополоцк, ул. Блохина, 29

Тел.: +375 214 42-30-31

Ihnatsyeva Sviatlana Aleksandrovna, Postgraduate at the Department of Computing Systems and Networks

211440, Novopolotsk, Blokhina St., 29

Tel.: +375 214 42-30-31

s.ignatieva@pdu.byБогушР. П.BohushR. P.

д. т. н., доцент, заведующий кафедрой вычислительных систем и сетей

Полоцк

Dr. of Sci. (Tech.), Associate Professor, Head of the Department of Computing Systems and Networks

Polotsk

Полоцкий государственный университет имени Евфросинии ПолоцкойEuphrosyne Polotskaya State University of Polotsk2023220620232138795Copyright © Игнатьева С.А., Богуш Р.П., 20232023Игнатьева С.А., Богуш Р.П.Ihnatsyeva S.A., Bohush R.P.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://doklady.bsuir.by/jour/article/view/3651

Для повышения точности работы системы реидентификации людей предлагается комплексный подход при формировании обучающей выборки для свёрточных нейронных сетей, предполагающий использование нового набора изображений, увеличение количества тренировочных примеров за счет существующих баз данных и применение ряда преобразований для повышения их разнообразия. Созданный набор данных PolReID1077 содержит изображения людей, которые были получены во все времена года, что позволит повысить корректность работы систем реидентификации при смене сезонов. ПреимуществомPolReID1077 является также использование видеоданных, полученных при внешнем и внутреннем наблюдении в большом количестве различных мест съемки. Поэтому изображения людей в созданном наборе характеризуются вариабельностью фона, яркостных и цветовых характеристик. Объединение созданного набора с существующими CUHK02, CUHK03, Market-1501, DukeMTMC-ReID и MSMT17 позволило получить 109 772 изображения для обучения. Увеличение разнообразия сформированных примеров достигается за счет применения к ним циклического сдвига, исключения цветности и замещения фрагмента уменьшенной копией другого изображения. Представлены результаты исследований по оценке точности реидентификации для свёрточных нейронных сетей ResNet-50 и DenseNet-121 при их тренировке с использованием предложенного подхода для формирования обучающей выборки.

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.

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The authors declare that there are no conflicts of interest present.