Title: Target detection and recognition method based on embedded vision

Authors: Xiao Zhao; Qi Zhou; Zhenjia Chen

Addresses: School of Information and Communication Engineering, Hainan University, Haikou 570228, Hainan, China ' School of Information and Communication Engineering, Hainan University, Haikou 570228, Hainan, China ' School of Information and Communication Engineering, Hainan University, Haikou 570228, Hainan, China

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.

Keywords: embedded vision; vehicle recognition; feature extraction.

DOI: 10.1504/IJWMC.2022.126364

International Journal of Wireless and Mobile Computing, 2022 Vol.23 No.2, pp.146 - 152

Received: 16 Sep 2021
Accepted: 17 Mar 2022
Published online: 24 Oct 2022 *

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