Towards parameter-optimized vessel re-identification based on IORnet

Amir Ghahremani, Yitian Kong, Egor Bondarev, Peter H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)


Reliable vessel re-identification would enable maritime surveillance systems to analyze the behavior of vessels by drawing their accurate trajectories, when they pass along different camera locations. However, challenging outdoor conditions and varying viewpoint appearances combined with the large size of vessels limit conventional methods to obtain robust re-identification performance. This paper employs CNNs to address these challenges. In this paper, we propose an Identity Oriented Re-identification network (IORnet), which improves the triplet method with a new identity-oriented loss function. The resulting method increases the feature vector similarities between vessel samples belonging to the same vessel identity. Our experimental results reveal that the proposed method achieves 81.5% and 91.2% on mAP and Rank1 scores, respectively. Additionally, we report experimental results with data augmentation and hyper-parameters optimization to facilitate reliable ship re-identification. Finally, we provide our real-world vessel re-identification dataset with various annotated multi-class features to public access.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2019 - 19th International Conference, 2019, Proceedings
EditorsJack J. Dongarra, João M.F. Rodrigues, Pedro J.S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Peter M.A. Sloot
Place of PublicationCham
Number of pages12
ISBN (Electronic)978-3-030-22750-0
ISBN (Print)978-3-030-22749-4
Publication statusPublished - 8 Jun 2019
Event19th International Conference on Computational Science, ICCS 2019 - Faro, Portugal
Duration: 12 Jun 201914 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11540 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Computational Science, ICCS 2019


  • CNNs
  • Maritime surveillance
  • Re-identification of vessels
  • Vessel re-identification dataset


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