A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment
المؤلفون المشاركون
Zhang, Jinyue
Zi, Lijun
Hou, Yuexian
Wang, Mingen
Jiang, Wenting
Deng, Da
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-05
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
In order to support smart construction, digital twin has been a well-recognized concept for virtually representing the physical facility.
It is equally important to recognize human actions and the movement of construction equipment in virtual construction scenes.
Compared to the extensive research on human action recognition (HAR) that can be applied to identify construction workers, research in the field of construction equipment action recognition (CEAR) is very limited, mainly due to the lack of available datasets with videos showing the actions of construction equipment.
The contributions of this research are as follows: (1) the development of a comprehensive video dataset of 2,064 clips with five action types for excavators and dump trucks; (2) a new deep learning-based CEAR approach (known as a simplified temporal convolutional network or STCN) that combines a convolutional neural network (CNN) with long short-term memory (LSTM, an artificial recurrent neural network), where CNN is used to extract image features and LSTM is used to extract temporal features from video frame sequences; and (3) the comparison between this proposed new approach and a similar CEAR method and two of the best-performing HAR approaches, namely, three-dimensional (3D) convolutional networks (ConvNets) and two-stream ConvNets, to evaluate the performance of STCN and investigate the possibility of directly transferring HAR approaches to the field of CEAR.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Jinyue& Zi, Lijun& Hou, Yuexian& Wang, Mingen& Jiang, Wenting& Deng, Da. 2020. A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1123060
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Jinyue…[et al.]. A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment. Advances in Civil Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1123060
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Jinyue& Zi, Lijun& Hou, Yuexian& Wang, Mingen& Jiang, Wenting& Deng, Da. A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1123060
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1123060
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر