Evaluation of oil workers' performance based on surveillance video

Elena Lebedeva, Andrey Zubkov, Denis Bondarenko, Konstantin Rymarenko, Marat Nukhaev, Sergey Grishchenko

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

We present our research on the applicability of computer vision techniques for extracting various oil workers' performance metrics. This paper focuses on learning two metrics associated with the workers' location. The first metric \boldsymbol{e}-{1} is the percent of frames in which only some part of the crew is present. If its value is bigger than some threshold value, the crew's performance is declared inefficient. We propose to perform human detection in each video frame and count people present in order to calculate \boldsymbol{e}-{1}. The Faster R-CNN and single-shot detectors with several types of feature extractors were tested on a specially collected dataset. By finetuning the most accurate of them we've achieved 0.99 precision and 0.91 recall. The second metric \boldsymbol{e}-{2} considers workers' distance from an automated gas control system, which is the main subject of maintenance. We propose using some markers on the uniform for worker recognition and estimation of his/her position relative to an automated gas control system. We've tested the ArUco and the RUNETag markers on synthetic data and proved that they cannot be applied to our problem. We've also carried out some preliminary research on uniform numbers detection, as they can be also considered as markers. The Connectionist Text Proposal Network (CTPN) used for text detection achieved an accuracy of 0.76. Text recognition performed by Tesseract OCR failed with 0.05 recall. However, we plan to collect a dataset for number detection and recognition in the future and test more approaches.

Язык оригиналаанглийский
Название основной публикацииSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы432-435
Число страниц4
ISBN (электронное издание)9781728144016
DOI
СостояниеОпубликовано - окт 2019
Событие2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Российская Федерация
Продолжительность: 21 окт 201927 окт 2019

Серия публикаций

НазваниеSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

Конференция

Конференция2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
СтранаРоссийская Федерация
ГородNovosibirsk
Период21.10.201927.10.2019

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  • 1.02 КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ НАУКИ

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