Proposal-driven segmentation for videos
Publication Type
Journal Article
Publication Date
8-2019
Abstract
Effectively utilizing the common information in a set of video frames is a vital aspect in video segmentation. However, existing methods that transport the common information from a prior frame to the current frame do not make use of the common information effectively. In order to address this issue, we apply a new strategy that jointly segments object through a convolutional neural network (CNN) to build a proposal-driven framework for exploiting the common information between two video frames by processing two video frames simultaneously in this letter. Moreover, proposals from the video frames are found useful for refining the segmentation results through fusing their segmentation results with the ones of the video frames. In our framework, proposals with features are generated by a faster region-CNN, and the L2 loss function is used to establish proposal pairs among proposals from the two selected frames. A new trained ResNet then keeps proposal pairs, which contain the same content, and the PSPNet model for segmentation is utilized to generate the segmentation results belonging to the frames and proposals. Finally, the proposals' segmentation results are refined using the video frames' segmentation results. The VOT 2016 segmentation dataset, the DAVIS 2017 dataset, and the SegTrack v2 dataset were used for training and testing our framework. Experimental results show that our proposal-driven segmentation framework is able to achieve higher accuracies in video segmentation challenge compared to those of the existing video segmentation methods.
Keywords
Segmentation, proposals, convolutional neural network (CNN)
Discipline
Information Security
Research Areas
Information Systems and Management
Publication
IEEE Signal Processing Letters
Volume
26
Issue
8
First Page
1098
Last Page
1102
ISSN
1070-9908
Identifier
10.1109/LSP.2019.2921654
Publisher
Institute of Electrical and Electronics Engineers
Citation
LI, Junliang; HE, Shengfeng; WONG, Hon-Cheng; and LO, Sio-Long.
Proposal-driven segmentation for videos. (2019). IEEE Signal Processing Letters. 26, (8), 1098-1102.
Available at: https://ink.library.smu.edu.sg/sis_research/7876
Additional URL
https://doi.org/10.1109/LSP.2019.2921654