期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2016
卷号：4
期号：3
页码：4334
DOI：10.15680/IJIRCCE.2016.0403175
出版社：S&S Publications
摘要：The Vehicle detection is method to detect the vehicles in the image or video data. The vehicle detection is the branch of the object detection, where the vehicle is the primary object. The vehicle detection can be performed on various kinds of the vehicle data obtained from the horizontal, aerial, parking or road surveillance cameras. In this paper, the vehicle det ection and classification method has been proposed by using the hybrid deep neural network over the image data and video obtained from the aerial and satellite images to determine the vehicle density. The non - negative matrix factorization (NMF) will be uti lized for the feature extraction and compression for the purpose of vehicle detection and classification. The 2 nd level feature compression will be performed to create the quick response vehicle detection and classification system. The proposed model will be programmed to detect the maximum vehicles visible as full or partial object in the image. The vehicle density reporting, vehicle movement reporting and upside & downside reporting for highways will be performed to achieve the goal of the vehicle detection and classification. The a im of this research is to produce the robust algorithm to detect and analyze the vehicle features in the images and videos with higher accuracy and precision
关键词：Vehicledetection;compressedfeature extraction; ANN object detection;Multi-class detection; Classification
