Open AccessResearch on Vehicle Detection Technology of Blind Spots at Night Based on CAdaBoost Algorithm
Author(s) -
Yong Cao,
Botao Wang
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 - 10.1088/1742-6596/1682/1/012037
Subject(s) - blind spot , haar like features , computer science , artificial intelligence , haar , computer vision , visibility , classifier (uml) , edge detection , algorithm , pattern recognition (psychology) , image processing , image (mathematics) , face detection , physics , optics , wavelet , facial recognition system
Aiming at the problem of vehicle video detection in blind areas at night, a method of vehicle detection in blind areas at night based on CAdaBoost algorithm is proposed. Since night vehicles and daytime vehicles have different illumination, visibility, vehicle edge characteristics, vehicle contours, etc., the test image is first grayed out and ROI is determined. Then, because the features of the car head and wheels at night are more obvious than other features, a parallel method is used to train multiple weak classifiers offline at the same time, mainly to train the Haar-like features of the car head and wheels of the test image. In the meantime, in order to improve the accuracy of detection, the weighted parameter is introduced to determine the role of the weak classifier in the final strong classifier. Then, the offline trained model is used to match the Haar-like features of the test vehicle’s car head and wheels respectively, and finally realize the real-time detection of vehicles in the blind area. The results show that: for vehicle detection in blind spots at night, the algorithm has a high detection accuracy and low detection time.