Construction Site Surveillance System
Date Issued
2015
Date
2015
Author(s)
Chen, Wei-Cheng
Abstract
Numerous construction site accidents have happened around the world in recently years. According to the Ministry of Labor, the occurrence rate of severe occupational injury in construction industry is much higher than others. This high risk is primarily caused by the deficiency of the personal protective equipment (PPE). In this thesis, we apply to the technique of body detection and object recognition on PPE checking system to examine whether construction workers are equipped as prescribed or not. As the result, the rate of construction hazard could be reduced. There are three parts in our system which including image preprocessing, feature extraction and recognition. First, videos of workers are taken by an IP camera. Then, the moving foreground images would be extracted by background subtraction, and the positions of head and body are located by the height of the foreground image. Lastly, the Support vector machine (SVM) is utilized to perform classification on the features which are hue histogram, saturation histogram and local binary pattern (LBP). The experiment results show the system could effectively recognize the safety hats and safety vests with the accuracies of 97% and 93%, respectively.
Subjects
clothing recognition
hard hat detection
vest detection
torso proportions analysis
Type
thesis
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