Abstract
In this paper, we present a pedestrian detection method based on the combination of Histograms of Oriented Gradient (HOG) feature and uniform local binary pattern (LBP) feature, which can detect pedestrian accurately. To the problem of low recognition rate for a single feature, we combine contour information and texture information, and propose the cascade of the two types of features, HOG features and LBP features as the feature set. In order to compare the experimental results, Gentle AdaBoost is used to train the pedestrian classifier on the INRIA dataset. The experimental results show that these two features of pedestrian detection algorithm improve the accuracy and reduce the error rate. Our method achieve a detection rate of 94.05% at FPPW = 10 -4 , which is better than Dalal’s (detection rate of 84% to 89% at 10 -4 FPPW).
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