Robust tracking of vessels in oceanographic airborne images

Matos, Jorge; Bernardino, Alexandre; Ribeiro, Ricardo

doi:10.1109/oceans.2016.7761468

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…They build a convolutional long short-term memory (LSTM) to learn those features and increase ship detection rates. In [16] the authors propose a method capable of performing ship detection in challenging surveillance conditions. They track vessels with a correlation filter complemented with image segmentation.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Cascaded Model for Ship Segmentation in Aerial Images

2022

Self Cite

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In this work, we propose a new method for real-time ship segmentation during maritime surveillance missions using aircrafts with onboard video cameras. We propose a cascade model with a detection stage followed by a segmentation stage. The detection stage selects candidate regions (bounding boxes) likely to contain ships. These bounding boxes are passed to the segmentation stage, where the ship segmentation mask is then obtained. By focusing the segmentation effort only in the image regions recommended by the previous detection stage, it is possible to improve the overall image processing time and, simultaneously, to obtain a better segmentation score than the one obtained by monolithic segmentation models that are trained in an end-to-end way. Additionally, we test the viability of using a Conditional Random Field model as final boundary refinement stage: although such model can improve the segmentation results when a full segmentation approach is used, our experiments did not show any significant improvements when using our proposed cascade model. We trained the detection and segmentation models with aerial ship images from publicly available maritime datasets. We tested the cascade model on the Airbus ship detection challenge, showing real-time performance and accurate maritime ship segmentation, comparable to state-of-the-art results.

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Section: Related Workmentioning

confidence: 99%

An Efficient Cascaded Model for Ship Segmentation in Aerial Images

2022

Self Cite

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“…) is shown as (7). We can obtain the circulant kernel matrix C(b) by cyclically shifting the vector b, which indeed provides us sufficient samples for training robust KCF tracker (see (8)).…”

Section: A Ship Tracking With Kcf Modelmentioning

confidence: 99%

“…Bolme et al proposed an adaptive correlation filter for accomplishing the visual ship tracking task by transforming the initial ship image sequences into frequency domain [6]. Matos et al combined an adaptive correlation filter with local re-detection features to obtain high-fidelity ship positions [7]. Similar researches can also be found in [8]- [12].…”

Section: Introductionmentioning

confidence: 98%

Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos

Chen

Yang

et al. 2020

IEEE Access

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Ship tracking provides crucial on-site microscopic kinematic traffic information which benefits maritime traffic flow analysis, ship safety enhancement, traffic control, etc., and thus has attracted considerable research attentions in the maritime surveillance community. Conventional ship tracking methods yield satisfied results by exploring distinct visual ship features in maritime images, which may fail when the target ship is partially or fully sheltered by obstacles (e.g., ships, waves, etc.) in maritime videos. To overcome the difficulty, we propose an augmented ship tracking framework via the kernelized correlation filter (KCF) and curve fitting algorithm. First, the KCF model is introduced to track ships in the consecutive maritime images and obtain raw ship trajectory dataset. Second, the data anomaly detection and rectification procedure are implemented to rectify the contaminated ship positions. For the purpose of performance evaluation, we implement the proposed framework and another three popular ship tracking models on the four typical ship occlusion videos. The experimental results show that our proposed framework successfully tracks ships in maritime video clips with high accuracy (i.e., the average root mean square error (RMSE), root mean square percentage error (RMSPE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE) are less than 10), which significantly outperforms the other popular ship trackers.INDEX TERMS Smart ship, curve fitting, kernelized correlation filter, visual ship tracking, ship occlusion.

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“…Since images taken from video sequences during maritime surveillance missions are highly correlated, ref. [ 11 ] propose a tracking method based on correlation filters complemented with image segmentation. A blob analysis stage is added to compensate for drifts in the correlation filter, which allows to re-center the target into the tracking window.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets

Ribeiro

Damas

Bernardino

2022

Sensors

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This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation network able to produce fast and reliable preliminary segmentation results and a post-processing 3D fully connected Conditional Random Field, which significantly improves segmentation results by exploring temporal correlations between nearby frames in video sequences. Moreover, due to the absence of maritime datasets consisting of properly labeled video sequences, we create a new dataset comprising synthetic video sequences of maritime surveillance scenarios (MarSyn). The main advantages of this approach are the possibility of generating a vast set of images and videos, being able to represent real-world scenarios without the necessity of deploying the real vehicle, and automatic labels, which eliminate human labeling errors. We train the system with the MarSyn dataset and with aerial footage from publicly available annotated maritime datasets to validate the proposed approach. We present some experimental results and compare them to other approaches, and we also illustrate the temporal stability provided by the second stage in missing frames and wrong segmentation scenarios.

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Robust tracking of vessels in oceanographic airborne images

Cited by 5 publications

References 28 publications

An Efficient Cascaded Model for Ship Segmentation in Aerial Images

An Efficient Cascaded Model for Ship Segmentation in Aerial Images

Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos

Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets

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