Pedestrian Detection Based on Combination of Candidate Region Location and HOG-CLBP Features

The pedestrian detection algorithm based on the histogram of orientation gradient (HOG) feature and the local binary pattern (LBP) operator adopts the sliding window search strategy. The scanning area is too large and the calculation is complex, which will cause the detection speed to be slow. In view of this, a pedestrian detection algorithm is proposed. First, a selective search algorithm is used to locate the target area, and the aspect ratio of the candidate area is limited to a certain range to filter out invalid windows. Then, in order to make up for the defects of LBP operator in texture expression, a complete local binary pattern (CLBP) operator is introduced to improve the expression ability of texture features. Then, considering that the dimensionality of the HOG feature and the CLBP operator is too high to affect the recognition ability of the classifier, the principal component analysis method is used to reduce the dimensionality of the HOG feature and the CLBP, respectively, and series fusion is conducted after dimension reduction. Finally, the mining process of hard examples is introduced to train the support vector machine classifier, which can make the model more fully trained and thus reduce the false detection rate. The simulation results on the INRIA dataset show that the proposed algorithm has a certain improvement in recognition rate and recognition speed.