کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
241898 1362712 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Vision-based action recognition of construction workers using dense trajectories
ترجمه فارسی عنوان
مبتنی بر چشم انداز به رسمیت شناختن عمل از کارگران ساختمانی با استفاده از مدار های انبوه
کلمات کلیدی
کارگر؛ به رسمیت شناختن عمل؛ ساخت و ساز؛ بینایی کامپیوتر؛ مدار انبوه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Wide spread monitoring cameras on construction sites provide large amount of information for construction management. The emerging of computer vision and machine learning technologies enables automated recognition of construction activities from videos. As the executors of construction, the activities of construction workers have strong impact on productivity and progress. Compared to machine work, manual work is more subjective and may differ largely in operation flow and productivity among different individuals. Hence only a handful of work studies on vision based action recognition of construction workers. Lacking of publicly available datasets is one of the main reasons that currently hinder advancement. The paper studies worker actions comprehensively, abstracts 11 common types of actions from 5 kinds of trades and establishes a new real world video dataset with 1176 instances. For action recognition, a cutting-edge video description method, dense trajectories, has been applied. Support vector machines are integrated with a bag-of-features pipeline for action learning and classification. Performances on multiple types of descriptors (Histograms of Oriented Gradients – HOG, Histograms of Optical Flow – HOF, Motion Boundary Histogram – MBH) and their combination have been evaluated. Discussion on different parameter settings and comparison to the state-of-the-art method are provided. Experimental results show that the system with codebook size 500 and MBH descriptor has achieved an average accuracy of 59% for worker action recognition, outperforming the state-of-the-art result by 24%.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advanced Engineering Informatics - Volume 30, Issue 3, August 2016, Pages 327–336
نویسندگان
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