Open AccessMOSnet: moving object segmentation with convolutional networks
Author(s) -
Jeong J.,
Yoon T.S.,
Park J.B.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.3982
Subject(s) - artificial intelligence , computer science , segmentation , computer vision , motion blur , motion (physics) , convolutional neural network , intersection (aeronautics) , object (grammar) , process (computing) , image segmentation , pixel , image (mathematics) , complement (music) , pattern recognition (psychology) , engineering , biochemistry , chemistry , complementation , gene , phenotype , aerospace engineering , operating system
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 state‐of‐the‐art methods, with respect to intersection over union and F ‐measure scores.