Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 2
611-614,630

Multispectral video object detection based on improved online stochastic tensor decomposition

Yang Guoliang
Yu Dingling
Lin Jianbin
Wang Yang
School of Electrical Engineering & Automation, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2019.10.0642

Abstract

For most current mono or tricolor cameras surveillance videos, it is difficult to solve the problems of light intensity changes, color saturation and shadows in object detection, and processing multi-spectral images of hundreds of bands will increase the amount of calculation. This paper proposed an improved online stochastic tensor decomposition algorithm for multispectral video moving object detection. On the basis of online stochastic tensor decomposition extensions, it used the online optimization method for real-time processing, combined with small sample batch initialization to narrow base matrix size, used the K-bilateral random projection constantly to update base part and sparse coefficient matrix. And it dealed a video frame until the set number of iterations had reached or all of the samples had been completed. Experiments on MSVS dataset show that the detection results and the quantitative evaluation based on F-measure, recall and precision have good results. Compared with the existing similar methods, it has more superior performance in detection accuracy and running speed.

Foundation Support

国家自然科学基金资助项目(51365017)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.10.0642
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 2
Section: Technology of Graphic & Image
Pages: 611-614,630
Serial Number: 1001-3695(2021)02-057-0611-04

Publish History

[2021-02-05] Printed Article

Cite This Article

杨国亮, 喻丁玲, 林剑彬, 等. 改进的在线随机张量分解的多光谱视频目标检测 [J]. 计算机应用研究, 2021, 38 (2): 611-614,630. (Yang Guoliang, Yu Dingling, Lin Jianbin, et al. Multispectral video object detection based on improved online stochastic tensor decomposition [J]. Application Research of Computers, 2021, 38 (2): 611-614,630. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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