Abstract
Vehicles have become an essential means of transportation in people's daily life. A large number of vehicles need scientific and effective detection and management. The existing vehicle recognition technology may not be able to achieve good results when applied in the actual environment of the embedded platform. We proposed a vehicle detection and recognition model for embedded platform and applied it to the actual environment. Support Vector Machine (SVM) uses Histogram of Oriented Gradient (HOG) feature combined with window sliding detection to complete vehicle detection. Convolutional Neural Network (CNN) is used to realise licence plate recognition. Furthermore, oriented fast and rotated brief (ORB) feature extraction method is used to extract vehicle key information quickly and accurately. Licence plate information and vehicle ORB features are stored on the embedded device as the unique features of the vehicle to identify the recorded vehicles. The model has good timeliness, high accuracy and practical value.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Wireless and Mobile Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
