中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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
DOI10.1109/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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