基于元胞自动机的动态背景运动目标检测

陆牧; 朱明; 高扬; 张刘

doi:10.3788/OPE.20172507.1934

您当前的位置：

首页 >

文章列表页 >

基于元胞自动机的动态背景运动目标检测

信息科学 | 更新时间：2020-07-07

- 基于元胞自动机的动态背景运动目标检测
- Moving target detection based on dynamic background of cellular automaton
- 光学精密工程 2017年25卷第7期页码：1934-1940
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所航空光学成像与测量重点实验室, 吉林长春 130033
  2.吉林大学仪器科学与电气工程学院, 吉林长春 1360061
- 作者简介：
  
  [ "陆牧(1989-), 男, 吉林长春人, 博士研究生, 2012年于吉林大学获得学士学位, 主要从事图像处理、目标检测、目标跟踪等方面的研究。E-mail:980443913@qq.com" ]
  [ "朱明(1964-), 男, 江西南昌人, 研究员, 博士生导师, 1985年于南京航空学院获得学士学位, 1991年于中国科学院长春光机所获得硕士学位, 主要从事图像处理, 光电成像测量技术, 电视跟踪和自动目标识别技术等方面的研究.E-mail:zhu_mingca@163.com" ]
- 基金信息：
  
  吉林省科技发展计划青年科研基金资助项目(20150520057JH)
- DOI：10.3788/OPE.20172507.1934
  中图分类号： TP391.41
- 收稿日期：2016-10-10，
  
  录用日期：2016-12-21，
  
  纸质出版日期：2017-07-25
- 稿件说明：
移动端阅览
陆牧, 朱明, 高扬, 等. 基于元胞自动机的动态背景运动目标检测[J]. 光学精密工程, 2017,25(7):1934-1940.

Mu LU, Ming ZHU, Yang GAO, et al. Moving target detection based on dynamic background of cellular automaton[J]. Optics and precision engineering, 2017, 25(7): 1934-1940.
陆牧, 朱明, 高扬, 等. 基于元胞自动机的动态背景运动目标检测[J]. 光学精密工程, 2017,25(7):1934-1940. DOI： 10.3788/OPE.20172507.1934.

Mu LU, Ming ZHU, Yang GAO, et al. Moving target detection based on dynamic background of cellular automaton[J]. Optics and precision engineering, 2017, 25(7): 1934-1940. DOI： 10.3788/OPE.20172507.1934.

摘要

针对传统运动目标检测算法在动态背景条件下难以准确检测出运动目标的问题，提出了一种基于元胞自动机的动态背景运动目标检测算法。首先，根据SLIC算法分割视频图像，并应用多模态混合动态纹理模型对视频图像进行背景建模。然后，融合空时显著性检测与基于元胞自动机的自动更新机制得到优化的显著性图。最后，通过对优化后的显著性图做适当的阈值分割处理得到视频图像中的运动目标。实验仿真结果表明，在动态背景条件下该算法可以有效的抑制视频图像中非运动目标的显著性物体对检测结果带来的影响，检测运动目标的精度较高，并且具有一定的鲁棒性。

Abstract

Aiming at the problem that it is hard to use the traditional moving target detection algorithm to accurately detect the moving target under the dynamic background

a kind of moving target detection algorithm for the cellular automaton under the dynamic background was proposed in the thesis. Firstly

according to SLIC algorithm

video images were divided in the thesis

and multi-mode hybrid dynamic texture model was used for background modeling for video images; Then

space-time salience detection was integrated with the optimized salience map which was obtained based on the automatic updating mechanism of the cellular automaton; Finally

through making appropriate threshold segmentation process for the optimized salience map

moving targets in video images was obtained. The experimental simulation result shows that under dynamic background

the algorithm can effectively restrain the influence of the salient object for non moving targets in video images on the detection result; moving targets can be detected with higher accuracy; what's more

the algorithm has certain robustness.

关键词

Keywords

references

JIANG B W, ZHANG L H, LU H C, et al.. Saliency detection via absorbing Markov chain[C]. 2013 IEEE International Conference on Computer Vision (ICCV), IEEE, 2013:1665-1672.

聂海涛, 龙科慧, 马军, 等.采用改进尺度不变特征变换在多变背景下实现快速目标识别[J].光学精密工程, 2015, 23(8):2349-2356.

NIE H T, LONG K H, MA J, et al.. Fast object recognition under multiple varying background using improved SIFT method[J]. Opt. Precision Eng., 2015, 23(8):2349-2356. (in Chinese)

YANG J M, YANG M H. Top-down visual saliency via joint CRF and dictionary learning[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012:2296-2303.

SUN J, LING H B. Scale and object aware image retargeting for thumbnail browsing[C]. 2011 IEEE International Conference on Computer Vision (ICCV), IEEE, 2011:1511-1518.

SHEN X H, WU Y. A unified approach to salient object detection via low rank matrix recovery[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012:853-860.

李静宇, 刘艳滢, 田睿, 等.视频监控系统中的概率模型单目标跟踪框架[J].光学精密工程, 2015, 23(7):2093-2099.

LI J Y, LIU Y Y, TIAN R, et al.. Probabilistic model single target tracking framework for video surveillance system[J]. Opt. Precision Eng., 2015, 23(7):2093-2099. (in Chinese)

ZHU W J, LIANG S, WEI Y C, et al.. Saliency optimization from robust background detection[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2014:2814-2821.

赖作镁, 王敬儒, 张启衡.背景运动补偿和假设检验的目标检测算法[J].光学精密工程, 2007, 15(1):112-116.

LAI Z M, WANG J R, ZHANG Q H. Object detection algorithm based on background motion compensation and hypothesis test[J]. Opt. Precision Eng., 2007, 15(1):112-116. (in Chinese)

YANG C, ZHANG L H, LU H C, et al.. Saliency detection via graph-based manifold ranking[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2013:3166-3173.

YAN Q, XU L, SHI J P, et al.. Hierarchical saliency detection[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2013:1155-1162.

WEI Y C, WEN F, ZHU W J, et al.. Geodesic saliency using background priors[C]. Computer Vision-ECCV 2012, Springer, 2012:29-42.

XIE Y L, LU H C. Visual saliency detection based on Bayesian model[C]. 201118th IEEE International Conference on Image Processing (ICIP), IEEE, 2011:645-648.

XIE Y L, LU H C, YANG M H. Bayesian saliency via low and mid level cues[J]. IEEE Transactions on Image Processing, 2013, 22(5):1689-1698.

WANG L, XUE J R, ZHENG N N, et al.. Automatic salient object extraction with contextual cue[C]. 2011 IEEE International Conference on Computer Vision (ICCV), IEEE, 2011:105-112.

TONG N, LU H C, ZHANG Y, et al.. Salient object detection via global and local cues[J]. Pattern Recognition, 2015, 48(10):3258-3267, doi:10.1016/j.patcog.2014.12.005.

SUN J, LU H C, LI S F. Saliency detection based on integration of boundary and soft-segmentation[C]. 201219th IEEE International Conference on Image Processing (ICIP), IEEE, 2012:1085-1088.

CHAN A B, VASCONCELOS N. Mixtures of dynamic textures[C]. 10th IEEE International Conference on Computer Vision, 2005, 1:641-647.

ACHANTA R, SHAJI A, SMITH K, et al.. SLIC superpixels[R]. EPFL Technical Report 149300, 2010.

JIANG H Z, WANG J D, YUAN Z J, et al.. Salient object detection:a discriminative regional feature integration approach[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2013:2083-2090.

VON NEUMANN J. The general and logical theory of automata[J]. Cerebral Mechanisms in Behavior, 1951, 10(3):1-41.

CHENG M M, WARRELL J, LIN W Y, et al.. Efficient salient region detection with soft image abstraction[C]. 2013 IEEE International Conference on Computer Vision (ICCV), IEEE, 2013:1529-1536.

JIANG P, LING H B, YU J Y, et al.. Salient region detection by UFO:Uniqueness, focusness and objectness[C]. 2013 IEEE International Conference on Computer Vision (ICCV), IEEE, 2013:1976-1983.

WANG G, CHEN Y G, YANG S C, et al. Salient target detection in remote sensing image via cellular automata[C]. 20156th International Conference on Intelligent Control and Information Processing (ICICIP), IEEE, 2015:417-420.

JIANG Z L, DAVIS L S. Submodular salient region detection[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2013:2043-2050.

LI X H, LU H C, ZHANG L H, et al.. Saliency detection via dense and sparse reconstruction[C]. 2013 IEEE International Conference on Computer Vision (ICCV), IEEE, 2013:2976-2983.

LI Y, HOU X D, KOCH C, et al.. The secrets of salient object segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2014:280-287.

浏览量

420

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

集成视觉显著性和群决策的航空零件孔特征检测

结合分数阶显著性检测及量子烟花算法的NSST域图像融合

结合Retinex校正和显著性的主动轮廓图像分割

光学遥感图像中复杂海背景下的舰船检测

基于距离矩阵的星图运动目标检测