Classification of Ships in Surveillance Video

Abstract

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