Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on th...
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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.
The limitations of Global Navigation Satellite System (GNSS) in enclosed spaces have prompted the need for alternative methods for determining the position and navigation of Unmanned Aerial Vehicles (UAVs). One potent...
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ISBN:
(纸本)9798350344004
The limitations of Global Navigation Satellite System (GNSS) in enclosed spaces have prompted the need for alternative methods for determining the position and navigation of Unmanned Aerial Vehicles (UAVs). One potential solution is to employ visual-based sensors and artificial intelligence (AI) algorithms. This study aims to explore the feasibility of a monocular vision-based positioning system for determining the position of a UAV by placing a sensor in a fixed position and feeding the image stream to an AI algorithm to generate the position and navigation solution. The results of this study could have practical applications, particularly inspection hangars where GNSS cannot be used. Automating the inspection process using UAVs could also help reduce the risk of human injury associated with elevated working spaces by improving the safety and efficiency of the inspection process.
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