Optimized Pedestrian Detection Algorithm for Norm-DP Model

Abstract

The traditional deep pyramid model attracts much attention as an effective pedestrian detection algorithm. It combines deformable part model and convolutional neural network model. However, the algorithm adopted in the feature extraction section has different pixel area sizes, so the models cannot be fully fused. The detection result is not ideal when it comes to the situation with a large number of pedestrians, complex postures, and occlusions. Therefore, a deep pyramid model algorithm based on normalization function (Norm-DP) is proposed in this paper. This algorithm combines the deformable part model and the convolutional neural network model, which extracts positive and negative samples directly from the pyramid features. Model training is then conducted on a latent variable support vector machine. The positioning frame is optimized through soft-non-maximum suppression (soft-NMS) algorithm and bounding box regression (BBR) algorithm. Experimental verification is performed on INRIA and MS COCO datasets. As a result, the detection accuracy of the proposed algorithm is higher than the optimal deformable part model algorithm, convolutional neural network algorithm, deep pyramid model algorithm and convolutional neural network algorithm combined with region selection in the situation with many pedestrians, complex postures and occlusions.

Keywords

• convolutional neural network (cnn)
• deformable part model algorithm
• normalization deep pyramid (norm-dp)
• soft-non-maximum suppression (soft-nms)
• bounding box regression (bbr)