###### Abstract  
In the study, an automated visualization of the proximity between workers and equipment is developed to manage workers' safety at construction sites using the convolutional-neural-network-based image processing of a closed-circuit television video. The images are analyzed to automatically transform a hazard index visualized in the form of a plane map. The graphical representation of personalized proximity in the plane map is proposed and termed as safety ellipse in the study. The safety ellipse depending on the posture of workers and the area occupied by the hazardous objects (trucks) enable to represent precise proximity. Collision monitoring is automated with computer vision techniques of artificial-intelligence-based object detection, occupied space calculation, pose estimation, and homography. Building construction; collision monitoring; proximity; safety ellipse; posture-dependent perception; image processing [color=black]0.0000-0. In the case of 3D cameras such as stereo vision and Kinect, it is possible to obtain the direct distance from the camera to the object, so the object is recognized by using the difference in the distance between the background and the object [11; 12]. In addition to location monitoring, workers' behavior such as posture and movement influence the occurrence of accidents. Being a precursor to injuries and accidents, unsafe behavior is associated with workers' stress and fatigue [13]. Wearable sensors attached to the wrists, feet, and waist of workers collect various information related to workers' behavior; for example, workers' actions can be detected using an accelerometer [14; 15] and the health status of workers using musculoskeletal sensors [16]. Image processing is also an alternative to behavior and safety monitoring: it is possible to estimate changes in workers' behavior from the changes in the detected object's size in a sequence of images [17] and to record working behavior through the movement of the worker's joints [18]. With the advancement of high-performance deep learning technology, recent studies on worker safety monitoring through the vision sensing of image processing have been conducted [19]. Deep learning enables image processing, such as detecting workers, equipment, and materials [20; 21], extracting the motion of workers, and detecting abnormal behaviors that cause accidents; some examples of deep learning are convolutional neural network (CNN)-based multiple worker detection and tracking [22], the detection of workers without hard hats [23], the detection of unauthorized workers [24], the analysis of workers' postures in various poses and changing backgrounds [25], the recognition of beams under construction and the detection of workers at risk of falling [26], and worker behavior analysis using recurrent neural network models [27]. When confining to collision monitoring, deep learning-based image processing for the detection and location of workers and equipment as well as the detection of abnormal behaviors of workers has been extended to the study of estimating the occurrence of collisions and the possibility of accidents of workers. A series of research achievements can be found in the literature; predicting the probability of collision from the distance calculated as the center coordinates of workers and equipment tends to underestimate real distance since the volume of equipment is ignored [22]; a direct use of the bounding box size, which is a result of deep learning-based detection, is suggested to consider the volume of equipment [28]. However, an intrinsic error induced due to the perspective being dependent on the position and angle of the camera exists, which reduces the accuracy of the detection of the volume of equipment.