Background Subtraction With Real-Time Semantic Segmentation
文献类型：期刊论文
| 作者 | D.D.Zeng; X.Chen; M.Zhu; M.Goesele; A.Kuijper |
| 刊名 | Ieee Access
 |
| 出版日期 | 2019 |
| 卷号 | 7页码:153869-153884 |
| 关键词 | Background subtraction,foreground object detection,semantic,segmentation,video surveillance,density-estimation,Computer Science,Engineering,Telecommunications |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/access.2019.2899348 |
| 英文摘要 | Accurate and fast foreground (FG) object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel BGS framework with the real-time semantic segmentation. Our proposed framework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background (BG) models and segments FG objects. The real-time semantic segmenter S is used to refine the FG segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame I-t, the BGS segmenter B computes a preliminary FG/BG mask B-t. At the same time, the real-time semantic segmenter S extracts the object-level semantics S-t. Then, some specific rules are applied on B-t and S-t to generate the final detection D-t. Finally, the refined FG/BG mask D-t is fed back to update the BG model. The comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves the state-of-the-art performance among all unsupervised BGS methods while operating at the real-time and even performs better than some deep learning-based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization. |
| 语种 | 英语 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/62833]  |
| 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,X.Chen,M.Zhu,et al. Background Subtraction With Real-Time Semantic Segmentation[J]. Ieee Access,2019,7:153869-153884. |
| APA | D.D.Zeng,X.Chen,M.Zhu,M.Goesele,&A.Kuijper.(2019).Background Subtraction With Real-Time Semantic Segmentation.Ieee Access,7,153869-153884. |
| MLA | D.D.Zeng,et al."Background Subtraction With Real-Time Semantic Segmentation".Ieee Access 7(2019):153869-153884. |
入库方式： OAI收割
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