Tae-Hun Woo, Goo-Tae Kwon, Chang-Yong Lee, So-Young Kwon, Sung-Young Kim, Young-Hyung Kim, Yong-Hwan Lee
{"title":"Image Processing Hardware Algorithm for Monitoring Environment","authors":"Tae-Hun Woo, Goo-Tae Kwon, Chang-Yong Lee, So-Young Kwon, Sung-Young Kim, Young-Hyung Kim, Yong-Hwan Lee","doi":"10.1109/WIECON-ECE.2017.8468916","DOIUrl":null,"url":null,"abstract":"Recently, object recognition applications using cameras in various devices have been developed and it is expected to be used in many fields in the future. In this paper, we designed SURF based hardware to detect forest fire. We designed the hardware and used a parallel processing structure to enable real-time processing. We also proposed a method to reduce memory usage to reduce the area of hardware. We have designed the hardware in HDL and verified its operation on Modelsim simulation comparing to the result of Matlab software. This design enables real-time monitoring of environments such as fire detection and air pollution.","PeriodicalId":188031,"journal":{"name":"2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIECON-ECE.2017.8468916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Recently, object recognition applications using cameras in various devices have been developed and it is expected to be used in many fields in the future. In this paper, we designed SURF based hardware to detect forest fire. We designed the hardware and used a parallel processing structure to enable real-time processing. We also proposed a method to reduce memory usage to reduce the area of hardware. We have designed the hardware in HDL and verified its operation on Modelsim simulation comparing to the result of Matlab software. This design enables real-time monitoring of environments such as fire detection and air pollution.