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R.S. Moreira, N.F.F. Ebecken, "Tracking Targets in the Sea Surface using a Weightless Neural Network Approach", in , (Editors), "Proceedings of the Ninth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 84, 2014. doi:10.4203/ccp.105.84

Keywords: neural networks, segmentation, binarization, colour models, tracking, surface targets.

Summary

This paper presents a method of tracking sea surface targets in video using the WiSARD weightless neural network approach. The tracking of objects in video is an important and challenging task in many applications. Difficulties can arise arising from weather conditions, target trajectory and appearance, occlusions, lighting conditions and noise. Tracking is a high-level application and requires the object location frame by frame in real time. At each frame, a tracker based on detection by segmentation performs three main steps: detection, tracking and analysis of the object characteristics. These steps need a good segmentation quality and the tracking performed by the WiSARD neural network depending on the image binarization quality. This paper proposes a fast hybrid binarization (thresholding and edge detection) in YcbCr colour model and procedures to configure a WiSARD neural network to improve efficiency when binarization errors occur.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 105 PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: Paper 84 Tracking Targets in the Sea Surface using a Weightless Neural Network Approach R.S. Moreira and N.F.F. Ebecken COPPE/UFRJ, Federal University of Rio de Janeiro, Brazil doi:10.4203/ccp.105.84 purchase the full-text of this paper Full Bibliographic Reference for this paper R.S. Moreira, N.F.F. Ebecken, "Tracking Targets in the Sea Surface using a Weightless Neural Network Approach", in , (Editors), "Proceedings of the Ninth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 84, 2014. doi:10.4203/ccp.105.84 Keywords: neural networks, segmentation, binarization, colour models, tracking, surface targets. Summary This paper presents a method of tracking sea surface targets in video using the WiSARD weightless neural network approach. The tracking of objects in video is an important and challenging task in many applications. Difficulties can arise arising from weather conditions, target trajectory and appearance, occlusions, lighting conditions and noise. Tracking is a high-level application and requires the object location frame by frame in real time. At each frame, a tracker based on detection by segmentation performs three main steps: detection, tracking and analysis of the object characteristics. These steps need a good segmentation quality and the tracking performed by the WiSARD neural network depending on the image binarization quality. This paper proposes a fast hybrid binarization (thresholding and edge detection) in YcbCr colour model and procedures to configure a WiSARD neural network to improve efficiency when binarization errors occur. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £45 +P&P)
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