Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/70451
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dc.contributor.authorPanick Chiewcharnen_US
dc.contributor.authorVarin Chouvatuten_US
dc.date.accessioned2020-10-14T08:31:07Z-
dc.date.available2020-10-14T08:31:07Z-
dc.date.issued2020-01-01en_US
dc.identifier.other2-s2.0-85084044569en_US
dc.identifier.other10.1109/KST48564.2020.9059371en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85084044569&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/70451-
dc.description.abstract© 2020 IEEE. Road accidents are caused by a variety of reasons. Having something cutting in front of the car is one of the causes of the accident. Although the current hardware technology may be available to detect objects to prevent the accidental cause, such technology is still expensive, comparing to relying upon only a front camera. We thus propose a real-time analysis of images captured from a front camera to detect objects cutting in front of the car and create a warning system for the purpose. Our method of detecting and tracking objects names the single shot multi box detector, used with pre-trained object detection models. We contribute situations where objects moving with x-axis acceleration and how our regions of interest are determined, based on the actual measurement. If the object has an acceleration in the x-axis and is in the regions of interest, the system will alert the driver to make the right decision in order to prevent road accidents.en_US
dc.subjectComputer Scienceen_US
dc.subjectDecision Sciencesen_US
dc.titleReal-time notification system of moving obstacles in vehicle-camera images using SSD and safe zonesen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleKST 2020 - 2020 12th International Conference on Knowledge and Smart Technologyen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsCenter of Excellence in Community Health Informaticsen_US
Appears in Collections:CMUL: Journal Articles

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