Part-based on-road vehicle detection using hidden random field  

作　　者：ZHANG XueTao HE YongJian WANG Fei 

机构地区：[1]Institute of Artificial Intelligence and Robotics, Xi＇an Jiaotong University, Xi＇an 710049, China [2]Xi＇an Communication Institute, Xi＇an 710049, China

出　　处：《Science China(Information Sciences)》2011年第12期2522-2529,共8页中国科学（信息科学）（英文版）

基　　金：supported partly by the National Natural Science Foundation of China(Grant No.90920301)

摘　　要：This paper addresses the problem of detecting on-road vehicles in still images captured by the on-board cameras. We model this as a labelling inference procedure and incorporate the part-based representation of the rear-ends of vehicle within a hidden random field based probabilistic model. Representing objects with parts inherently good for dealing with occlusions. In the proposed model, the part labels form a hidden layer in the graphical model. Our approaches can automatically find the latent parts without explicit indication during training. The experiment is performed on the database with real images with a promising result.

关 键 词：vehicle detection hidden random field part-based model 

分 类 号：TP316.7[自动化与计算机技术—计算机软件与理论] U469.3[自动化与计算机技术—计算机科学与技术]