Pixelwise Deep Sequence Learning for Moving Object Detection
文献类型:期刊论文
作者 | Chen, Yingying; Wang, Jinqiao; Zhu, Bingke; Tang, Ming; Lu, Hanqing |
刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY |
出版日期 | 2019-09-01 |
卷号 | 29期号:9页码:2567-2579 |
ISSN号 | 1051-8215 |
关键词 | Moving object detection background modeling moving object proposal convolutional neural networks |
DOI | 10.1109/TCSVT.2017.2770319 |
通讯作者 | Wang, Jinqiao(jqwang@nlpr.ia.ac.cn) |
英文摘要 | Moving object detection is an essential, well-studied but still open problem in computer vision and plays a fundamental role in many applications. Traditional approaches usually reconstruct background images with hand-crafted visual features, such as color, texture, and edge. Due to lack of prior knowledge or semantic information, it is difficult to deal with complicated and rapid changing scenes. To exploit the temporal structure of the pixel-level semantic information, in this paper, we propose an end-to-end deep sequence learning architecture for moving object detection. First, the video sequences are input into a deep convolutional encoder-decoder network for extracting pixel-wise semantic features. Then, to exploit the temporal context, we propose a novel attention long short-term memory (Attention ConvLSTM) to model pixelwise changes over time. A spatial transformer network and a conditional random field layer are finally appended to reduce the sensitivity to camera motion and smooth the foreground boundaries. A multi-task loss is proposed to jointly optimization for frame-based classification and temporal prediction in an end-to-end network. Experimental results on CDnet 2014 and LASIESTA show 12.15% and 16.71% improvement to the state of the art, respectively. |
资助项目 | National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61375035] |
WOS研究方向 | Engineering |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000489738900004 |
资助机构 | National Natural Science Foundation of China |
源URL | [http://ir.ia.ac.cn/handle/173211/26597] |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
通讯作者 | Wang, Jinqiao |
作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Yingying,Wang, Jinqiao,Zhu, Bingke,et al. Pixelwise Deep Sequence Learning for Moving Object Detection[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2019,29(9):2567-2579. |
APA | Chen, Yingying,Wang, Jinqiao,Zhu, Bingke,Tang, Ming,&Lu, Hanqing.(2019).Pixelwise Deep Sequence Learning for Moving Object Detection.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,29(9),2567-2579. |
MLA | Chen, Yingying,et al."Pixelwise Deep Sequence Learning for Moving Object Detection".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 29.9(2019):2567-2579. |
入库方式: OAI收割
来源:自动化研究所
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