Combining CRF and Deformable Part Model for Pedestrian Detection

Authors

Ma Ji, 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China;;2. School of Information, Liaoning University, Shenyang 110036, China;;
Jingjiao Li, 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China;;
Ma Li, 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China;;2. School of Information, Liaoning University, Shenyang 110036, China;;
Zhao Yue, 3. Bohai University, Jinzhou 121000, China;

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.

Recommended Citation

Ji, Ma; Li, Jingjiao; Li, Ma; and Yue, Zhao (2020) "Combining CRF and Deformable Part Model for Pedestrian Detection," Journal of System Simulation: Vol. 27: Iss. 10, Article 13.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss10/13

First Page

2310

Revised Date

2015-07-23

DOI Link

https://doi.org/

Last Page

2315

CLC

TP391

Recommended Citation

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.