ОЭММПУАвтоматика и телемеханика Automation and Remote Control

  • ISSN (Print) 0005-2310
  • ISSN (Online) 2413-9777

Повторная идентификация людей в системах видеонаблюдения с использованием глубокого обучения: анализ существующих методов

Код статьи
10.31857/S0005231023050057-1
DOI
10.31857/S0005231023050057
Тип публикации
Статья
Статус публикации
Опубликовано
Авторы
Том/ Выпуск
Том / Номер выпуска 5
Страницы
61-112
Аннотация
Статья посвящена многостороннему анализу повторной идентификации людей в системах видеонаблюдения и современных методов ее решения с использованием глубокого обучения. Рассматриваются общие принципы и применение сверточных нейронных сетей для этой задачи. Предложена классификация систем реидентификации. Приведен анализ существующих наборов данных для обучения глубоких нейронных архитектур, описаны подходы для увеличения количества изображений в базах данных. Рассматриваются подходы к формированию признаков изображений людей. Представлен анализ основных применяемых для реидентификации моделей архитектур сверточных нейронных сетей, их модификаций, а также методов обучения. Анализируется эффективность повторной идентификации на разных наборах данных, приведены результаты исследований по оценке эффективности существующих подходов в различных метриках.
Ключевые слова
реидентификация видеоданные сверточные нейронные сети метрики оценки точности дескрипторы изображений
Дата публикации
15.05.2023
Год выхода
2023
Всего подписок
0
Всего просмотров
6

Библиография

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