Feature fusion for front vehicle detection and implementation
Received date: 2016-02-06
Online published: 2017-12-30
To achieve robust and real-time detection of front vehicles, an algorithm is proposed based on morphological features and HAAR-like features of vehicles. To avoid interference of external environmental factors in the extraction of shadows underneath the vehicle, the monkey king genetic algorithm (MKGA) threshold segmentation is used. Regions of interest are obtained using morphological characteristics in the initial screening. HAAR-like features of these regions are extracted, and the dimension is reduced. In the secondary screening, an auto classifier trained by support vector machine (SVM) is input. A total of 300 frames are randomly selected to verify the algorithm. Experimental results show that the algorithm can detect vehicles in a complicated environment. Compared to single feature detection, accuracy is raised from 80% to 90%. The HAAR-like integral diagram and PCA for dimension reduction can effectively improve detection speed. The algorithm meets the driving assistance system requirements both in accuracy and speed.
LIU Dongjun, XU Meihua, GONG Luming, XIA Chenjun . Feature fusion for front vehicle detection and implementation[J]. Journal of Shanghai University, 2017 , 23(6) : 893 . DOI: 10.12066/j.issn.1007-2861.1767
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