Affiliation:
1. School of IoT Engineering (School of Information Security), Jiangsu Vocational College of Information Technology, Wuxi, China
Abstract
Moving object detection is still a challenging task in complex scenes. The existing methods based on deep learning mainly use U-Nets and have achieved amazing results. However, they ignore the local continuity between pixels. In order to solve this problem, a method based on a superpixel fusion network (SF-Net) is proposed in this article. First, the median filter is used to extract the candidate foreground (called
pixel features
) and the image sequence is segmented by superpixel. Then, the histogram features (called
superpixel features
) of the candidate foreground superpixels are extracted. Next, the pixel features and the superpixel features are the inputs of SF-Net, respectively. Experiments show the effectiveness of SF-Net on 34 image sequences and the average F-measure reaches 0.84. SF-Net can remove more background noise and has stronger expression ability than a network with the same depth.
Funder
Jiangsu Provincial Colleges and Universities Natural Science Research General Project
Research Project of Jiangsu Vocational College of Information Technology
2021 Jiangsu University Philosophy and Social Science Research Project
2021 Jiangsu Higher Education Teaching Reform Research Project
Water Conservancy Science and Technology Project of Jiangsu Province
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture
Cited by
7 articles.
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