{"title":"基于生成对抗网络的车载摄像头图像预测","authors":"J. Watanabe, T. Gonsalves","doi":"10.5121/CSIT.2019.90205","DOIUrl":null,"url":null,"abstract":"Moving object detection is one of the fundamental technologies necessary to realize autonomous driving. In this study, we propose the prediction of an in-vehicle camera image by Generative Adversarial Network (GAN). From the past images input to the system, it predicts the future images at the output. By predicting the motion of a moving object, it can predict the destination of the moving object. The proposed model can predict the motion of moving objects such as cars, bicycles, and pedestrians.","PeriodicalId":251548,"journal":{"name":"Computer Science & Information Technology(CS & IT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IN-VEHICLE CAMERA IMAGES PREDICTION BY GENERATIVE ADVERSARIAL NETWORK\",\"authors\":\"J. Watanabe, T. Gonsalves\",\"doi\":\"10.5121/CSIT.2019.90205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Moving object detection is one of the fundamental technologies necessary to realize autonomous driving. In this study, we propose the prediction of an in-vehicle camera image by Generative Adversarial Network (GAN). From the past images input to the system, it predicts the future images at the output. By predicting the motion of a moving object, it can predict the destination of the moving object. The proposed model can predict the motion of moving objects such as cars, bicycles, and pedestrians.\",\"PeriodicalId\":251548,\"journal\":{\"name\":\"Computer Science & Information Technology(CS & IT)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Science & Information Technology(CS & IT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/CSIT.2019.90205\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science & Information Technology(CS & IT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/CSIT.2019.90205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
IN-VEHICLE CAMERA IMAGES PREDICTION BY GENERATIVE ADVERSARIAL NETWORK
Moving object detection is one of the fundamental technologies necessary to realize autonomous driving. In this study, we propose the prediction of an in-vehicle camera image by Generative Adversarial Network (GAN). From the past images input to the system, it predicts the future images at the output. By predicting the motion of a moving object, it can predict the destination of the moving object. The proposed model can predict the motion of moving objects such as cars, bicycles, and pedestrians.
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