Browsing by Author "Lai, Chun-Yu"

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Toward Community Sensing of Road Anomalies Using Monocular Vision
(2016-04-15)
Chen, Hua-Tsung
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Lai, Chun-Yu
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Shih, Chun-An
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資訊工程學系
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Department of Computer Science
Advanced vehicle safety is an emerging issue appealed from the rapidly explosive population of car owners. Posing a remarkable safety threat, road anomalies not only damage vehicles but may also cause serious danger, especially at night or under bad visibility conditions. However, maintaining the quality of roadways has been a big challenge for municipalities around the world. Recently, the rapid development and reduced cost of digital cameras have made it economically feasible to deploy driving video recorders (DVRs) on vehicles. Thus, in this paper, we employ the widespread DVRs as distributed sensors with high mobility to conduct pervasive sensing of road anomalies. First, vehicle shakes are detected to infer the candidates of road anomalies. Then, we segment pavement regions, extract saliencies on the road surface, and classify whether a detected vehicle shake is caused by a road anomaly or an artificial speed bump. Experiments are conducted on a test data set collected by front-mounted DVRs, and the results verify that the proposed system can effectively detect road anomalies in real time, showing its good feasibility in real-world environments.
Vision-Based Road Bump Detection Using a Front-Mounted Car Camcorder
(2014-01-01)
Chen, Hua-Tsung
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Lai, Chun-Yu
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Hsu, Chun-Chieh
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Lee, Suh-Yin
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Lin, Bao-Shuh Paul
Advanced vehicle safety is a recently emerging issue, appealed from the rapidly explosive population of car owners. Increasing driver assistance systems have been designed for warning drivers of what should be noticed by analyzing surrounding environments with sensors and/or cameras. As one of the hazard road conditions, road bumps not only damage vehicles but also cause serious danger, especially at night or under poor lighting conditions. In this paper we propose a vision-based road bump detection system using a front-mounted car camcorder, which tends to be widespread deployed. First, the input video is transformed into a time-sliced image, which is a condensed video representation. Consequently, we estimate the vertical motion of the vehicle based on the time-sliced image and infer the existence of road bumps. Once a bump is detected, the location fix obtained from GPS is reported to a central server, so that the other vehicles can receive warnings when approaching the detected bumpy regions. Encouraging experimental results show that the proposed system can detect road bumps efficiently and effectively. It can be expected that traffic security will be significantly promoted through the mutually beneficial mechanism that a driver who is willing to report the bumps he/she meets can receive warnings issued from others as well.