Book ChapterDOI
Underwater Moving Object Detection Using GMG
Vrushali Rajaram Pagire,Anuradha C. Phadke +1 more
- pp 233-244
TLDR
In this article, background is categorized into three types i.e. static, moderate dynamic and high dynamic backgrounds and the GMG algorithm is implemented to detect moving object and compare the results for the three background categories.Abstract:
The development in ocean exploration and observation make the demand for moving object detection in underwater increasingly urgent. The moving object detection in underwater with dynamic or moving background is the challenging and difficult task for the researchers. In dynamic environment, foreground object as well as background of the scene both are in motion condition, thus it is very difficult to detect foreground moving object in underwater medium. In proposed work, background is categorized into three types i.e. static, moderate dynamic and high dynamic backgrounds. The GMG algorithm invented by Andrew B. Godbehere, Akihiro Matsukawa, Ken Goldberg is implemented to detect moving object and compare the results for the three background categories. The sensitivity and accuracy parameter values are considered for the comparison of the results.read more
Citations
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Proceedings ArticleDOI
Underwater Fish Detection and Classification using Deep Learning
TL;DR: The MobileNet model is utilised to detect and recognise the fish breed in the proposed work, which is based on the Kaggle dataset, which has nine different fish breeds in total.
Proceedings ArticleDOI
Underwater Fish Detection and Classification using Deep Learning
TL;DR: In this paper , the MobileNet model is used to detect and recognize the fish breed in the proposed work, which is based on the Kaggle dataset, which has nine different fish breeds in total.
References
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Book ChapterDOI
Deep learning on underwater marine object detection: A survey
TL;DR: It is concluded that there is a great scope for automation in the analysis of digital seabed imagery using deep neural networks, especially for the detection and monitoring of seagrass.
Journal ArticleDOI
Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model
TL;DR: A novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features and can accurately detect the dynamic background.
Journal ArticleDOI
A Reverse Bearings Only Target Motion Analysis for Autonomous Underwater Vehicle Navigation
Talmon Alexandri,Roee Diamant +1 more
TL;DR: The Reverse BO-TMA is suitable for the long-term deployment of an AUV and in cases where energy is scarce and cooperating anchors are not available, and is close to the posterior Cramér-Rao lower bound.