Combining CRF and Deformable Part Model for Pedestrian Detection

Abstract

Abstract: Pedestrian detection has been widely used in many fields. It is one of the focus in computer vision. The part-based detection method in the pedestrian detection shows excellent performance and has a strong adaptability in posture change of human body. But it is not good for Occlusion problem. When the Discriminative threshold is higher, miss rate is very high. Considering the disadvantage of LSVM method for mining hidden information, a two layers classifier was proposed based on the deformable parts model establishing conditional random field model for Occlusion problem. For learning model parameters, the stochastic gradient descent and belief propagation algorithm optimization objective function of the random field conditions were used. The experimental results show that the proposed approach achieves good effect for Occlusion problem.

Key words: pedestrian detection, deformable part model, CRF, LSVM

CLC Number:

TP391

Ma Ji, Li Jingjiao, Ma Li, Zhao Yue. Combining CRF and Deformable Part Model for Pedestrian Detection[J]. Journal of System Simulation, 2015, 27(10): 2310-2315.

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