Data fusion using improved Dempster-Shafer evidence theory for vehicle detection

被引：13
|
作者：
Zhao, Wentao [1 ]
Fang, Tao [1 ]
Jiang, Yan [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China
来源：
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 1, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/FSKD.2007.235
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Data fusion is an important tool for improving the performance of detecting system when various sensors are available. The Dempster-Shafer evidence theory for fusion has similar reasoning logic with human. So we apply the data fusion method which is based on Dempster-Shafer theory, in a vehicle detecting system to increase the detection accuracy. In this paper, the Dempster-Shafer evidence theory and its problem are discussed, and an improved Reliability Revaluated Dempster-Shafer Fusion (RRDSF) algorithm is proposed and applied The experiments show promising results and encourage us to do further work.
引用
收藏
页码：487 / 491
页数：5
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