Abstract
For maritime surveillance, collecting information about vessels and their behavior is of vital importance. This implies reliable vessel detection and determination of the viewing angle to a vessel, which can help in analyzing the vessel behavior and in re-identification. This paper presents a vessel classification and orientation recognition system for maritime surveillance. For this purpose, we have established two novel multi-class vessel detection and vessel orientation datasets, provided to open public access. Each dataset contains 10,000 training and 1,000 evaluation images with 31,078 vessel labels (10 vessel types and 5 orientation classes). We deploy VGG/SSD to train two separate CNN models for multi-class detection and for orientation recognition of vessels. Both trained models provide a reliable F1 score of 82% and 76%, respectively.
Original language | English |
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Title of host publication | IS&T International Symposium on Electronic Imaging 2019, Image Processing: Algorithms and Systems XVII |
Number of pages | 5 |
DOIs | |
Publication status | Published - 13 Jan 2019 |
Event | IS&T International Symposium on Electronic Imaging 2019, Image Processing: Algorithms and Systems XVII - Burlingame, United States Duration: 13 Jan 2019 → 17 Jan 2019 Conference number: XVII http://www.imaging.org/site/IST/IST/Conferences/EI/EI_2019/Conference/C_IPAS.aspx |
Conference
Conference | IS&T International Symposium on Electronic Imaging 2019, Image Processing: Algorithms and Systems XVII |
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Abbreviated title | IPAS2019 |
Country/Territory | United States |
City | Burlingame |
Period | 13/01/19 → 17/01/19 |
Internet address |