Water region detection supporting ship identification in port surveillance

Authors:
Xinfeng Bao;Svitlana Zinger;Rob Wijnhoven;Peter H. N. De With
Affiliations:
Video Coding and Architectures Research Group, Electrical Engineering Faculty, Eindhoven University of Technology, Eindhoven, MB, The Netherlands;Video Coding and Architectures Research Group, Electrical Engineering Faculty, Eindhoven University of Technology, Eindhoven, MB, The Netherlands;ViNotion B.V., Eindhoven, CH, The Netherlands;Video Coding and Architectures Research Group, Electrical Engineering Faculty, Eindhoven University of Technology, Eindhoven, MB, The Netherlands
Venue:
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Year:
2012

Citing 4
Cited 0

Efficient Graph-Based Image Segmentation

International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Horizon Detection Using Machine Learning Techniques

ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
The Pascal Visual Object Classes (VOC) Challenge

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a robust and accurate water region detection technique developed for supporting ship identification. Due to the varying appearance of water body and frequent intrusion of ships, a region-based recognition is proposed. We segment the image into perceptually meaningful segments and find all water segments using a sampling-based Support Vector Machine (SVM). The algorithm is tested on 6 different port surveillance sequences and achieves a pixel classification recall of 97.5% and precision of 96.4%. We also apply our water region detection to support the task of multiple ship detection. Combined with our cabin detector, it successfully removes 74.6% false detections generated in the cabin detection process. A slight decrease of 5% in the recall value is compensated by a significant improvement of 15% in precision.