Foreground detection in a multi-target fish tracking from video-recordings using U-net based architecture
One of the fundamental problems in computer vision is the backgroundforeground segmentation and most of the strategies have severe drawbacks when working with natural images, where there are extreme conditions such as illumination changes combined with sudden background differences or other noise; m...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | UVic-UCC |
| Repositorio: | RiUVic. Repositori institucional de la UVic-UCC |
| OAI Identifier: | oai:dspace.uvic.cat:10854/180603 |
| Acceso en línea: | http://hdl.handle.net/10854/180603 https://doi.org/10.3233/978-1-61499-918-8-381 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprenentatge profund (Aprenentatge automàtic) Videovigilància Xarxes neuronals |
| Sumario: | One of the fundamental problems in computer vision is the backgroundforeground segmentation and most of the strategies have severe drawbacks when working with natural images, where there are extreme conditions such as illumination changes combined with sudden background differences or other noise; moreover if the system has to face real-time restrictions. In this case authors focus on a variation of the U-net architecture to obtain the segmentation of the objects (fishes) in every single frame. The U-net has some interesting properties to explore in the case of image segmentation, such as multi-scale parameter combination. The reported preliminary results, working in a context of a multi-target fish tracking are promising, and envisions an approach that could provide a real-time response to long-lasting experiments using HQ video for multi-target tracking in real-time Computer vision systems. |
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