铁路客运站复杂环境中的运动目标检测

孙首群; 刘康亚; 刘硕妍; 吕晓军; 詹璇

doi:10.19818/j.cnki.1671-1637.2013.03.016

铁路客运站复杂环境中的运动目标检测

doi: 10.19818/j.cnki.1671-1637.2013.03.016

1.
上海理工大学机械工程学院, 上海 200093
2.
中国铁道科学研究院电子计算技术研究所, 北京 100081

基金项目:

国家863计划项目 2009AA11211

铁道部专项资金支持课题研究项目 J2011X007

上海市教委重点学科建设项目 J50503

详细信息

作者简介:
孙首群(1964-), 男, 河南郑州人, 上海理工大学副教授, 工学博士, 从事机电系统的电热耦合和系统动力学研究

中图分类号: U491.116
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- 被引次数: 0
出版历程
- 收稿日期: 2013-01-18
- 刊出日期: 2013-06-25

Moving target detection in complex environment of railway station

1.
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2.
Institute of Computing Technology, China Academy of Railway Sciences, Beijing 100081, China

More Information

Author Bio:
SUN Shourqun(1964-), male, associate professor, PhD, +86-21-55275056, jrssq@163.com

摘要

摘要: 采用分层组织的形式将传统高斯混合模型分为背景层、竞争层和噪声层, 各层分别采用不同的更新机制, 在各层之间引入晋级和降级机制以纠正可能存在的误判。采用基于轮廓检测的噪声滤波实现噪声层更新以消除噪声, 并利用直方图匹配检测伪前景区域以提高对背景变化的适应能力。使用停车场视频和铁路客运站候车室视频对改进后高斯混合模型的检测效果进行了验证。验证结果表明: 改进的高斯混合模型有效避免了长期静止的目标被融入背景, 降低了光线突变或摄像机噪声的干扰, 加快了背景改变时模型的更新速度, 目标检测速度比传统GMM提高了10%。检则方法满足了铁路客运站智能视频监控实时性和准确性的要求, 为视频分析奠定了基础。
- 运动目标检测 /
- 高斯混合模型 /
- 直方图匹配 /
- 噪声滤波 /
- 分层组织 /
- 轮廓检测
Abstract: Traditional GMM(Gaussian mixture model) was dived into background layer, completion layer and noise layer by using hierarchical organization.Diverse update mechanisms were applied in different layers. In order to correct possible misjudgment, promotion and downgraded mechanisms were introduced between layers. To eliminate noise, noise layer was updated by using noise filter based on contour detection. In order to improve the adaptability for changing background, pseudo foreground area was detected by using histogram matching. The detection effect of improved GMM was verified by using the videos of station and parking lot. Verification result indicates that the problem of long-term static target being merged into background is settled. The impact of light mutations or camera noise is reduced. The updating speed of model increases when the background changes. Detection speed increases by 10% compared with traditional GMM.The efficiency and accuracy of moving target detection in railway station are improved by improved GMM, and the foundation for intelligent video analysis is laid.
- moving target detection /
- Gaussian mixture model /
- histogram matching /
- noise filter /
- hierarchical organization /
- contour detection

HTML全文

图 1 改进的GMM算法

Figure 1. Improved GMM algorithm

下载: 全尺寸图片幻灯片

图 2 第150帧停车场图像中运动目标检测结果

Figure 2. Detection results of moving objects in image 150 of parking lot

下载: 全尺寸图片幻灯片

图 3 第500帧停车场图像中运动目标检测结果

Figure 3. Detection results of moving objects in image 500 of parking lot

下载: 全尺寸图片幻灯片

图 4 第800帧停车场图像中运动目标检测结果

Figure 4. Detection results of moving objects in image 800 of parking lot

下载: 全尺寸图片幻灯片

图 5 第1 200帧停车场图像中运动目标检测结果

Figure 5. Detection results of moving objects in image 1 200 of parking lot

下载: 全尺寸图片幻灯片

图 6 第300帧候车室图像中运动目标检测结果

Figure 6. Detection results of moving objects in image 300 of waiting room

下载: 全尺寸图片幻灯片

图 7 第800帧候车室图像中运动目标检测结果

Figure 7. Detection results of moving objects in image 800 of waiting room

下载: 全尺寸图片幻灯片

图 8 第1 200帧候车室图像中运动目标检测结果

Figure 8. Detection results of moving objects in image 1 200 of waiting room

下载: 全尺寸图片幻灯片

图 9 第2 500帧候车室图像中运动目标检测结果

Figure 9. Detection results of moving objects in image 2 500 of waiting room

下载: 全尺寸图片幻灯片

图 10 第3 500帧候车室图像中运动目标检测结果

Figure 10. Detection results of moving objects in image 3 500 of waiting room

下载: 全尺寸图片幻灯片

表 1 耗时对比

Table 1. Comparison of consuming times ms·帧^-1

视频	方法	最大耗时	最小耗时	平均耗时
停车场	传统GMM	328	296	313
	改进GMM	312	265	281
	传统GMM	468	156	235
	改进GMM	406	171	203

下载: 导出CSV

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