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- Title
MOSnet: moving object segmentation with convolutional networks.
- Authors
Jeong, J.; Yoon, T. S.; Park, J. B.
- Abstract
Identifying moving objects is considered a difficult problem owing to camera motion, motion blur and appearance changes. To solve these problems, a moving object segmentation method based on a convolutional neural network is presented. The proposed network takes successive image pairs as input, and predicts the per-pixel motion status. This process consists of three streams: one that learns appearance features, another that learns motion features and a third that combines both features. Therefore, a joint model is learned for segmenting a moving object, because appearance and motion features complement each other. Experimental results, based on a challenging dataset, demonstrate that the proposed method has superior performance over stateof- the-art methods, with respect to intersection over union and F-measure scores.
- Subjects
OBJECT recognition algorithms; CAMERAS; ARTIFICIAL neural networks; MOTION; MOTION detectors
- Publication
Electronics Letters (Wiley-Blackwell), 2018, Vol 54, Issue 3, p136
- ISSN
0013-5194
- Publication type
Article
- DOI
10.1049/el.2017.3982