Car Detection Using Codebook and Directed Graphical Model

摘要：

In this paper, we propose a Directed Graphical Model-based car detection method. Cars are represented by codebook, which is generated robust to surface marking. We modeled visual context into boosted MCMC to reduce the effect of background during object detection. Two kinds of spatial context (part-part, bjectbackground) and a hierarchical context (part-whole) are used. We incorporate these contexts into a directed graphical model that can provide car detection information in the form of figure-ground segmentation. The inference is conducted using multi-modal Markov Chain Monte Carlo (MCMC) sampling. Experimental results validate the power of the proposed framework for car detection especially in a cluttered environment.

关键词： car detection directed graphical model codebook representation boosted MCMC

作者: ZHANG Ying QIN Guang-jie

作者单位: School of Information Engineering, Changan University, Xian, China 710064 Software School, Xidian School of Information Engineering, Changan University, Xian, China 710064

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 530-534

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）