Non-interactive automatic video segmentation of moving targets

Yu ZHOU; An-wen SHEN; Jin-bang XU

doi:10.1631/jzus.C1200071

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Non-interactive automatic video segmentation of moving targets

计算机与自动化 | Updated：2022-12-09

- Non-interactive automatic video segmentation of moving targets
- Non-interactive automatic video segmentation of moving targets
- 信息与电子工程前沿（英文） 2012年13卷第10期页码：736-749
- Affiliations：
  
  Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- Author bio：
  
  [ "Yu ZHOU, E-mail: sherry.haku@gmail.com" ]
  Jin-bang XU, E-mail: xujinbang@mail.hust.edu.cn
- Funds：
  
  Project (No. 61033003) supported by the National Natural Science Foundation of China
- DOI：10.1631/jzus.C1200071
  中图分类号： TP751.1
- 收稿：2012-03-15，
  
  修回：2012-;9-11，
  
  纸质出版：2012-10
- Accepted：
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Non-interactive automatic video segmentation of moving targets[J]. 信息与电子工程前沿（英文）, 2012,13(10):736-749.

Yu ZHOU, An-wen SHEN, Jin-bang XU. Non-interactive automatic video segmentation of moving targets[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(10): 736-749.
Non-interactive automatic video segmentation of moving targets[J]. 信息与电子工程前沿（英文）, 2012,13(10):736-749. DOI： 10.1631/jzus.C1200071.

Yu ZHOU, An-wen SHEN, Jin-bang XU. Non-interactive automatic video segmentation of moving targets[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(10): 736-749. DOI： 10.1631/jzus.C1200071.

摘要

Abstract

Extracting moving targets from video accurately is of great significance in the field of intelligent transport. To some extent

it is related to video segmentation or matting. In this paper

we propose a non-interactive automatic segmentation method for extracting moving targets. First

the motion knowledge in video is detected with orthogonal Gaussian-Hermite moments and the Otsu algorithm

and the knowledge is treated as foreground seeds. Second

the background seeds are generated with distance transformation based on foreground seeds. Third

the foreground and background seeds are treated as extra constraints

and then a mask is generated using graph cuts methods or closed-form solutions. Comparison showed that the closed-form solution based on soft segmentation has a better performance and that the extra constraint has a larger impact on the result than other parameters. Experiments demonstrated that the proposed method can effectively extract moving targets from video in real time.

关键词

Keywords

references

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