Research on Pedestrian Detection Method Based on Improved DPM Model

Abstract

Abstract:

In order to improve the accuracy of pedestrian detection in complex environment, the paper proposed a pedestrian detection
method based on improved DPM model. When the feature is extracted, to reduce the dimension of the normalized feature, the reduced
dimension method is used instead of the principal component analysis, and the gradient direction of the image unit is discretized into 9
intervals. The pedestrian is divided into several modules based on body parts, and the features of pedestrian and each modules are
extracted. Then, the root model and module model are obtained by classifier training for pedestrians and modules respectively. Finally,
the Cascade method is used instead of the traditional dynamic programming method for pedestrian detection. The results show that
with the method of simulation in Matlab environment, the recognition rate is significantly improved compared with the traditional
detection method based on HOG feature, and compared with the original DPM method, it improves the detection speed to a certain
extent without reducing the recognition rate.

Key words: pedestrian detection, HOG, DPM, Cascade method

CLC Number:

TN941

Zhang Yaxu, Long Hui,Yun Lijun. Research on Pedestrian Detection Method Based on Improved DPM Model[J]. , 2018, 3(6): 13-18.

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