Combining background subtraction algorithms with convolutional neural network
文献类型：期刊论文
| 作者 | D.D.Zeng; M.Zhu; A.Kuijper |
| 刊名 | Journal of Electronic Imaging
 |
| 出版日期 | 2019 |
| 卷号 | 28期号:1页码:6 |
| 关键词 | foreground object detection,convolutional neural network,CDnet 2014,dataset,video surveillance,object detection,Engineering,Optics,Imaging Science & Photographic Technology |
| ISSN号 | 1017-9909 |
| DOI | 10.1117/1.Jei.28.1.013011 |
| 英文摘要 | Accurate and fast extraction of foreground objects is a key prerequisite for a wide range of computer vision applications, such as object tracking and recognition. Thus, many background subtraction (BGS) methods for foreground object detection have been proposed in recent decades. However, this is still regarded as a tough problem due to a variety of challenges, such as illumination variations, camera jitter, dynamic backgrounds, and shadows. Currently, there is no single method that can handle all the challenges in a robust way. We try to solve this problem from a perspective of combining different state-of-the-art BGS algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder-decoder fully convolutional neural network architecture is adapted and trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different BGS algorithms and produce a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single BGS algorithms. We show that our solution is more efficient than other BGS combination strategies. (C) 2019 SPIE and IS&T |
| 语种 | 英语 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/62832]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | D.D.Zeng,M.Zhu,A.Kuijper. Combining background subtraction algorithms with convolutional neural network[J]. Journal of Electronic Imaging,2019,28(1):6. |
| APA | D.D.Zeng,M.Zhu,&A.Kuijper.(2019).Combining background subtraction algorithms with convolutional neural network.Journal of Electronic Imaging,28(1),6. |
| MLA | D.D.Zeng,et al."Combining background subtraction algorithms with convolutional neural network".Journal of Electronic Imaging 28.1(2019):6. |
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