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The computer vision-based detection of construction workers in images or videos is necessary for the safety managements and productivity of construction workers. Researchers in previous studies, detecting construction workers in the construction scene via computer vision techniques, have considered various features such as motion, shape, and color. Due to the pose changes of the workers, construction worker detection using body shape as a feature in the construction scene remains a challenging task. This study proposes a safety vest detection method, as a preceding method of the construction worker detection, which uses the motion of workers and the color pixels of safety vests for distinguishing from others in the construction scene independent of the workers' pose changes. The background subtraction method is performed by using an approximate median filter for the purpose of reducing the candidate regions that the color pixel classification will be performed as a sequential step. Then, the color pixel classification method is performed with a comparative analysis of two color spaces (Lab and HSV

The development of 3D reconstruction from 2D building images enables cost-effective and accurate acquisition of spatial data. Feature extraction is a fundamental technique for 3D reconstruction method. However, buildings mostly consist of planar surfaces whose entities are featureless. This study presents 3D building reconstruction using A-KAZE feature extraction algorithm. Because A-KAZE algorithm does not use Gaussian blurring like SIFT and SURF, A-KAZE algorithm has potential to extract correct visual features for feature matching and 3D reconstruction. The proposed method was tested on actual building scenes acquired from a high-resolution camera. The experimental results showed that the A-KAZE algorithm can detect the sufficient number of features with low computation time. It is expected that the proposed method can be implemented in comprehensive 3D reconstruction of civil infrastructures.