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A Framework for Automated Progress Monitoring Based on hog Feature Recognition and High Resolution Remote Sensing Image


Affiliations
1 School of Economics and Management, North China Electric Power University, Beijing, PCR, China
 

Construction target monitoring became one of the key concerns of the construction field. Computer vision based monitoring methods like 2D image, video and 3D laser scan methods have difficulties in meeting real-time and data objectivity requirements. This paper points out the applicability of high resolution remote sensing image (HRRSI) in this task. This paper proposes a way for enhancing Histogram of Gradients (HOG) feature for HRRSI. HOG is applied as the key feature for machine learning, which is carried out by the boosting method. To overcome the complexity of HOGs in HRRSIs, this paper builds a feature framework according to the top, side and height characteristics of engineering ground objects. Based on the feature framework, this paper presents a HRRSI based framework for construction progress monitoring. Through an application case, the performance of the framework is presented. Judging from the result, this method is robust under different seasonal and illuminative conditions.

Keywords

Network Object Recognition, HOG Feature, High Resolution Remote Sensing Image, Progress Monitoring, Construction Project.
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  • A Framework for Automated Progress Monitoring Based on hog Feature Recognition and High Resolution Remote Sensing Image

Abstract Views: 482  |  PDF Views: 179

Authors

Xu Ruhang
School of Economics and Management, North China Electric Power University, Beijing, PCR, China

Abstract


Construction target monitoring became one of the key concerns of the construction field. Computer vision based monitoring methods like 2D image, video and 3D laser scan methods have difficulties in meeting real-time and data objectivity requirements. This paper points out the applicability of high resolution remote sensing image (HRRSI) in this task. This paper proposes a way for enhancing Histogram of Gradients (HOG) feature for HRRSI. HOG is applied as the key feature for machine learning, which is carried out by the boosting method. To overcome the complexity of HOGs in HRRSIs, this paper builds a feature framework according to the top, side and height characteristics of engineering ground objects. Based on the feature framework, this paper presents a HRRSI based framework for construction progress monitoring. Through an application case, the performance of the framework is presented. Judging from the result, this method is robust under different seasonal and illuminative conditions.

Keywords


Network Object Recognition, HOG Feature, High Resolution Remote Sensing Image, Progress Monitoring, Construction Project.

References