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Connected Vehicle Insights. Trends in Computer Vision: An Overview of Vision-based Data Acquisition and Processing Technology and Its Potential for the Transportation Sector. Technology Scan Series 20
| 题名： | Connected Vehicle Insights. Trends in Computer Vision: An Overview of Vision-based Data Acquisition and Processing Technology and Its Potential for the Transportation Sector. Technology Scan Series 20 |
| 作者： | Bayless, S. H.; Guan, A.; Neelakantan, R. |
| 关键词： | Computer Vision Applications; Drivers; Potential Hazards; Safety Risks; Vehicles |
| 摘要： | Drivers must keep their eyes on the road, but can always use some assistance in maintaining their awareness and directing their attention to potential emerging hazards. In the last decade, the auto industry and the auto aftermarket have experimented with devices that provide drivers with a second pair of electronic eyes, enabled by simple vision-based data acquisition and processing technology. In 2000, Iteris introduced one of the first commercially available large-scale computer vision applications, lane departure warning, in Mercedes Actros trucks.1 Since then, a number of computer-based vision products have been made available in vehicles and, just recently, in aftermarket automotive devices. By contrast, road operators have for a long time used computer vision to monitor and analyze the performance of their highway networks. Computer vision is the process of using an image sensor to capture images, then using a computer processor to analyze these images to extract information of interest. A simple computer vision system can indicate the physical presence of objects within view by identifying simple visual attributes such as shape, size, or color of an object. More sophisticated computer vision systems may establish not only the presence of an object, but can increasingly identify (or classify) the object based upon the requirements of an application. In intelligent transportation systems (ITS), computer vision technology is broadly applied to either (1) detect objects and events that may represent safety risks to drivers, or (2) detect hindrances to mobility or otherwise improve the efficiency of road networks. |
| 报告类型： | 科技报告 |
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