IoT-based 3D convolution for video salient object detection

doi:10.1007/s00521-018-03971-3

	IoT-based 3D convolution for video salient object detection
	Dong, Shizhou 1,2; Gao, Zhifan 3; Pirbhulal, Sandeep 1; Bian, Gui-Bin4 ; Zhang, Heye 5; Wu, Wanqing 1; Li, Shuo 3
发表期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
	2020-02-01
卷号	32 期号:3 页码:735-746
通讯作者	Bian, Gui-Bin(guibin.bian@ia.ac.cn)
摘要	The video salient object detection (SOD) is the first step for the devices in the Internet of Things (IoT) to understand the environment around them. The video SOD needs the objects' motion information in contiguous video frames as well as spatial contrast information from a single video frame. A large number of IoT devices' computing power is not sufficient to support the existing SOD methods' expensive computational complexity in emotion estimation, because they might have low hardware configurations (e.g., surveillance camera, and smartphone). In order to model the objects' motion information efficiently for SOD, we propose an end-to-end video SOD algorithm with an efficient representation of the objects' motion information. This algorithm contains two major parts: a 3D convolution-based X-shape structure that directly represents the motion information in successive video frames efficiently, and 2D densely connected convolutional neural networks (DenseNet) with pyramid structure to extract the rich spatial contrast information in a single video frame. Our method not only can maintain a small number of parameters as the 2D convolutional neural network but also represents spatiotemporal information uniformly that enables it can be trained end-to-end. We evaluate our proposed method on four benchmark datasets. The results show that our method achieves state-of-the-art performance compared with the other five methods.
关键词	Internet of Things Salient object detection Video processing Deep learning
DOI	10.1007/s00521-018-03971-3
关键词[WOS]	SEGMENTATION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000512022900010
出版者	SPRINGER LONDON LTD
七大方向——子方向分类	多模态智能
引用统计	被引频次：27[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38338
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Bian, Gui-Bin
作者单位	1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China 2.Univ Chinese Acad Sci Shenzhen, Shenzhen Coll Adv Technol, Shenzhen, Peoples R China 3.Western Univ, London, ON, Canada 4.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China 5.Sun Yat Sen Univ, Guangzhou, Guangdong, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Dong, Shizhou,Gao, Zhifan,Pirbhulal, Sandeep,et al. IoT-based 3D convolution for video salient object detection[J]. NEURAL COMPUTING & APPLICATIONS,2020,32(3):735-746.
APA	Dong, Shizhou.,Gao, Zhifan.,Pirbhulal, Sandeep.,Bian, Gui-Bin.,Zhang, Heye.,...&Li, Shuo.(2020).IoT-based 3D convolution for video salient object detection.NEURAL COMPUTING & APPLICATIONS,32(3),735-746.
MLA	Dong, Shizhou,et al."IoT-based 3D convolution for video salient object detection".NEURAL COMPUTING & APPLICATIONS 32.3(2020):735-746.