Open AccessThe Method of Vehicle Type Identification Based on Improved Corner Feature
Author(s) -
Tong Zhang,
Wen Bochong
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1438/1/012019
Subject(s) - corner detection , feature (linguistics) , mathematics , pattern recognition (psychology) , artificial intelligence , point (geometry) , eigenvalues and eigenvectors , matching (statistics) , wavelet , feature vector , type (biology) , identification (biology) , pixel , computer vision , image (mathematics) , computer science , geometry , statistics , physics , philosophy , linguistics , botany , biology , ecology , quantum mechanics
This paper present a method of vehicle type identification using improved corner points as key points for feature description. The corner points can reflect the information of different type. Therefore, we adopt the features of corner points to recognize which type the vehicle belongs to. The method of Harris corner detection was improved, and the ratio of eigenvalues of M matrix in different directions was added to the corner response. The corner points were detected by improved corner response and results of test showed that the count of the detected corner points was increased. Considering rotation invariance and scale invariance, feature description is carried out around detected corner points. The vector of feature is determined by the Harr wavelet response of the pixels around the corner point, but the direction is only one of the eight values, and the length of the vector is 16. The matching rate of feature vectors between different types is used to determine which type the vehicle belongs to. The results of experiment confirmed the validity of the method.