MOSnet: Moving object segmentation with convolutional networks

J. Jeong, T. S. Yoon, J. B. Park

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)136-138
Number of pages3
JournalElectronics Letters
Issue number3
Publication statusPublished - 2018 Feb 8

Bibliographical note

Funding Information:
Acknowledgment: This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (no. NRF-2015R1A2A2A01007545), and was partially supported by Microsoft Research.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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