![]() ![]() ![]() It usually occurs unconsciously and happens thousands of times a day without you ever realizing that you are using it. One way depth perception allows us to do this is by taking previous knowledge and using it to understand the world around us. This type of perception is responsible for forming an idea of the length, width and height of an object. Your two eyes look at the object from different angles and that information is processed in your brain to form a single image. It takes both eyes working in sync to look at an object and develop an informed idea about it, like its size or how far away it is. But what exactly is it?ĭepth perception is the human eye’s ability to see in three dimensions and judge the distance of an object. Maybe after your friend stubbed their toe, they grumbled about their depth perception, or maybe your little brother tried on your glasses and totally lost his depth perception. ![]() After defining the basic requirements for realistic 3D broadcasting services, we will cover various multi-modal immersive media processing technologies.We’ve all heard of the words “depth perception” before. In this tutorial lecture, we are going to cover the current state-of-the-art technologies for 3D video: representation of 3D scenes, acquisition of 3D video contents, illumination compensation and color correction, camera calibration and image rectification, depth map modeling and enhancement, 3-D warping and depth map refinement, coding of multi-view video and depth map, hole filling for occluded objects, and view synthesis using homography. Among them, multi-view video coding is the key technology for various applications including free-viewpoint video, free-viewpoint television, 3DTV, immersive teleconference, and surveillance systems. Since 3DTV is considered as the next generation broadcasting service that can deliver real and immersive experiences by supporting user-friendly interactions, a number of advanced three-dimensional video technologies have been studied. In recent years, various multimedia services have become available and the demand for three-dimensional television (3DTV) is growing rapidly. The PPE detection model had the average precision and recall of 91.25% and 94.77%, respectively. Based on three different test videos, the average precision and recall for the worker detection model were 99.67% and 92.92%, respectively. The experimental results demonstrate the practicality and robustness of the proposed method to detect PPE in far-field videos. The proposed method is applied to videos from a construction site. The first model detects the human bodies on the construction site, and the second one detects if the detected person is wearing a hardhat and a safety vest. This method uses a frame segmentation technique and a nested network with two Faster R-CNN models to detect safety noncompliances. This research proposes an approach for detecting if anyone on the construction site is wearing the required PPE, even when he or she is far from the surveillance cameras. Nevertheless, detecting workers and PPE is still a challenge in far-field videos. To facilitate safety monitoring, a large number of research studies applied computer vision for detecting PPE on construction sites. Currently, safety managers and inspectors on construction sites are responsible for monitoring safety regulations, which is a time-consuming task. However, workers may neglect to wear the required PPE while working, which subsequently increases the potential risk for accidents. Personal Protective Equipment (PPE) is a major part of safety regulations to prevent accidents. Safety is a main concern for the construction industry because of the high rate of accidents and casualties on construction sites. ![]()
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