{"title":"数据库软件中的车辆识别与编译","authors":"M. Madhumitha, P. Dhivya","doi":"10.1109/ICSCAN49426.2020.9262286","DOIUrl":null,"url":null,"abstract":"Vehicle Recognition from obtaining images in a motion platform is still challenging. The system would focus and capture attributes of vehicles like color, number plate and speed of the vehicle. The images are being captured from various CCTV systems through distributed intelligence along with time and location stamps. The database used to identify suspects from video clips of crime related CCTV footages. This can be achieved by optical character recognition (OCR) and algorithm based on regression YOLO (You Only Look Once). To recognize an vehicle features, Conda tool is used with Tensor flow and Keras framework.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"22 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Vehicle Recognition and Compilation in Database Software\",\"authors\":\"M. Madhumitha, P. Dhivya\",\"doi\":\"10.1109/ICSCAN49426.2020.9262286\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vehicle Recognition from obtaining images in a motion platform is still challenging. The system would focus and capture attributes of vehicles like color, number plate and speed of the vehicle. The images are being captured from various CCTV systems through distributed intelligence along with time and location stamps. The database used to identify suspects from video clips of crime related CCTV footages. This can be achieved by optical character recognition (OCR) and algorithm based on regression YOLO (You Only Look Once). To recognize an vehicle features, Conda tool is used with Tensor flow and Keras framework.\",\"PeriodicalId\":6744,\"journal\":{\"name\":\"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)\",\"volume\":\"22 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSCAN49426.2020.9262286\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCAN49426.2020.9262286","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在运动平台上获取图像进行车辆识别仍然具有挑战性。该系统将聚焦并捕捉车辆的颜色、车牌和速度等属性。这些图像是通过分布式智能从不同的闭路电视系统捕获的,并附有时间和地点戳。该数据库用于从与犯罪有关的闭路电视录像片段中识别嫌疑人。这可以通过光学字符识别(OCR)和基于YOLO (You Only Look Once)回归的算法来实现。为了识别车辆特征,将Conda工具与Tensor flow和Keras框架结合使用。
Vehicle Recognition and Compilation in Database Software
Vehicle Recognition from obtaining images in a motion platform is still challenging. The system would focus and capture attributes of vehicles like color, number plate and speed of the vehicle. The images are being captured from various CCTV systems through distributed intelligence along with time and location stamps. The database used to identify suspects from video clips of crime related CCTV footages. This can be achieved by optical character recognition (OCR) and algorithm based on regression YOLO (You Only Look Once). To recognize an vehicle features, Conda tool is used with Tensor flow and Keras framework.