Shadow Detection Based on Adaboost Classifiers in a Co-training Framework

摘要：

The problem of shadow detection is a challenging assignment in video surveillance systems.There are plentiful research achievements about shadow detection but they are not intellective owning to abundant manual input.In this paper, we describe a semi-supervised ensemble technique based on adaboost classifiers in a co-training framework.In this way to detect shadows just demand a fraction of labled datas,and then apply unlabled datas to enhance categorical performance.In the co-training framework,the two detectors are trained synchronously form independent viewpoints. Afterwards the unlabled datas with high confidence which are trained by one classifier are labled and appended to the training pool of the other one.These datas are extracted the information about color, edge,and luminance from RGB color space.Contrary to most of other methods,we increase the illumination assessment to forecast the probability of shadows existence.The experimental results which are operated on the standard roadway and indoor video sequences are ideal and comparable.

关键词： Shadow detection Adaboost classifiers Co-training

作者: Jie Zhao Suhong kong Guozun Men

作者单位: College of Electronic and Information Engineering, Hebei University, Baoding, China College of Economics, Hebei University, Baoding, China

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 1672-1676

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）