An intelligent video computing method for automated productivity analysis of cyclic construction operations

Gathering data for improving on-site operations is an essential and difficult task in construction. Among other methods, videotaping has long been used in construction to analyze construction operation. However, in the absence of an efficient video interpretation method, tedious manual reviewing is currently still required to extract productivity information from these videos. This paper presents a study on developing an intelligent video computing method to interpret videos of cyclic construction operations automatically into productivity information. More specifically, this research focuses on designing a mechanism for furthering the crosstalk between the prior knowledge of construction operations and computer vision techniques. It uses this mechanism to guide the detection and tracking of project resources as well as work state classifications and abnormal production scenario identifications. Experimental results from preliminary studies have shown the potential of the proposed video interpretation method as an improved productivity data analysis method.

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