Classification of ships in surveillance video

被引:0
|
作者
Luo, Qiming [1 ]
Khoshgoftaar, Taghi M. [1 ]
Folleco, Andres [1 ]
机构
[1] Florida Atlantic Univ, Dept Comp Sci & Engn, Boca Raton, FL 33431 USA
来源
IRI 2006: PROCEEDINGS OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION | 2006年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Object classification is an important component in a complete visual surveillance system. In the context of coastline surveillance, we present an empirical study on classifying 402 instances of ship regions into 6 types based on their shape features. The ship regions were extracted from surveillance videos and the 6 types of ships as well as the ground truth classification labels were provided by human observers. The shape feature of each region was extracted using MPEG-7 region-based shape descriptor. We applied k Nearest Neighbor to classify ships based on the similarity of their shape features, and the classification accuracy based on stratified ten-fold cross validation is about 91%. The proposed classification procedure based on MPEG-7 region-based shape descriptor and k Nearest Neighbor algorithm is robust to noise and imperfect object segmentation. It can also be applied to the classification of other rigid objects, such as airplanes, vehicles, etc.
引用
收藏
页码:432 / +
页数:4
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