Open AccessA Multi-feature Combination Method for Tracking of Marine Ships
Author(s) -
Lichun Yang,
Dan Yang,
Fan Wang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2216/1/012077
Subject(s) - computer science , artificial intelligence , computer vision , tracking (education) , feature (linguistics) , feature extraction , feature tracking , psychology , pedagogy , linguistics , philosophy
Maritime images and videos provide a variety of kinematic traffic information, including traffic volume, ship speeds and headings, which significantly benefits remote maritime traffic management and maritime safety improvement. To track multiple moving objects robustly from the complex original maritime image sequence, we propose an ensemble ship tracking framework with multiple feature extraction and Dempster–Shafer theory of evidence. Spatial features such as statistical features of gray values and geometric features in the image sequence rather than the motion characteristics are extracted, and the D-S theory is applied for combination of feature evidence to perform the correct association of multiple objects. We demonstrate the performance of the proposed ship tracking algorithm on a typical marine traffic scene through the marine surveillance video. Experimental results show that the proposed method can achieve continuous tracking of multiple marine moving objects with strong stability, and have the potential to improve marine transportation and fishery safety.