Title | Real time motion estimation using a neural architecture implemented on GPUs |
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Type | Journal article |
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Authors | Garcia-Rodriguez, J., Orts Escolano, S., Angelopoulou, A., Psarrou, A., Azorin-Lopez, J. and Garcia-Chamizo, J.M. |
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Abstract | This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis. |
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Keywords | Motion estimation |
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| Neural architectures |
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| Topology preservation |
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| Real time |
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| GPGPU |
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Journal | Journal of Real-Time Image Processing |
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Journal citation | 11 (4), pp. 731-749 |
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ISSN | 1861-8200 |
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Year | 2016 |
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Publisher | Springer |
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Accepted author manuscript | File Access Level Open (open metadata and files) |
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Publisher's version | File Access Level Open (open metadata and files) |
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Digital Object Identifier (DOI) | https://doi.org/10.1007/s11554-014-0417-y |
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Web address (URL) | http://www.scopus.com/inward/record.url?eid=2-s2.0-84897352114&partnerID=MN8TOARS |
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Publication dates |
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Published | 2016 |
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