# Image Processing and QR Code Application Method for Construction Safety Management
## 2 Literature Review  
### Construction Safety Management  
In recent years, as high-resolution cameras were popularized in construction sites at low prices, the resolution of various images (i.e., photographs and videos) generated from the site has been highly developed. Moreover, analysis research is being conducted to utilize artificial intelligence technology for multiple purposes in the construction field [11,12]. Currently, computer vision technology has been developed to a level that allows it to automatically analyze various construction activities [13, 14, 15, 16]. Accordingly, in safety management for construction, research on automation of safety management using computer vision technology shows a proportionate increase. In traditional safety management, safety accidents were considered to have been caused by individual carelessness, so personal responsibility was important. However, even the most experienced workers do not identify all of the safety risk situations that safety managers can [17]. Therefore, safety issues should be treated as separate from the worker's experience, construction skill, and workability, and cannot be defined as individual problems alone. As a result, corporate responsibility for construction safety management has been gradually emphasized.  
The safety management method for construction provides various advantages (e.g., reducing work injuries, risk control, productivity improvement, cost reduction, etc.) to construction projects [18]. However, it takes a lot of capital and time to observe these factors directly [19,20]. Fortunately, the low-cost prevalence of high-resolution cameras and advances in computer vision technology has shown the potential to solve these problems. To apply the technology to the right target, the computer must recognize what the observer sees and recognizes as the same. For this purpose, object recognition was conducted according to changes in color-related information (e.g., RGB values, hue, saturation) for accurate detection of objects [21]. Furthermore, as the resolution of surveillance cameras deployed at the construction site gradually improves, multiple workers are recognized in one video [22], and the path tracking of each worker also possible [23, 24, 25]. A study on human-object interactions (HOIs) [26] and a skeleton-based study [27] have been able to provide a basis for judging the current worker's stability by recognizing the work situation and pose of workers. As the classification of objects became clear, researchers tried to analyze the movement and activity of objects from 2D images [28, 29, 30, 31]. Moreover, by applying a methodology to estimate the 3D coordinates of an object from multiple images, a method that can implement an object's position on a three-dimensional plane has been studied without any sensor (e.g., distance sensor, ultrasonic sensor, etc.) [32; 33; 34; 35]. As such, the current computer vision studies have immediately discovered the management targets in the field and are performing the targets' location based on the distance between the surveillance camera and the object. In addition, most studies classify objects as simple class levels (e.g., workers, people) and do not try to realize their own information (e.g., worker's name). If the statistics enable workers to recognize the frequency of exposure to an unsafe situation, a perception of safety can be improved by themselves [36]. This immediate feedback system can also be applied at an economical cost to a system dynamics approach [37; 38] to determine whether the current governance structure is working properly.