[1]杨磊,任衍允,蔡纪源. 一种基于深度数据的高斯模型运动目标检测方法[J].计算机技术与发展,2015,25(09):27-30.
　YANG Lei,REN Yan-yun,CAI Ji-yuan. A Gaussian Model Moving Target Detection Method Based on Depth Data[J].,2015,25(09):27-30.
点击复制

 一种基于深度数据的高斯模型运动目标检测方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年09期
页码:
27-30
栏目:
智能、算法、系统工程
出版日期:
2015-09-10

文章信息/Info

Title:
 A Gaussian Model Moving Target Detection Method Based on Depth Data
文章编号:
1673-629X（2015)09-0027-04
作者:
 杨磊; 任衍允; 蔡纪源
 上海大学 机电工程与自动化学院；上海市电站自动化技术重点实验室
Author(s):
 YANG Lei; REN Yan-yun; CAI Ji-yuan
关键词:
 ; Kinect深度数据; 单高斯模型; 运动目标检测; 形态学滤波
Keywords:
 ; Kinect depth data; single Gauss model; moving target detection; morphological filtering
分类号:
TP301
文献标志码:
A
摘要:
 鉴于目前基于图像的运动目标检测系统对目标场景光照条件非常敏感，文中提出一种基于Kinect深度数据曲率的单高斯模型运动目标检测方法，增强了系统对场景采集误差的鲁棒性。首先对深度数据进行中值滤波，利用单高斯模型对目标区域深度数据进行建模；在对目标场景实时采集数据与背景参数进行高斯概率门限值判别后，经过形态学滤波，达到了运动目标检测的目的。同时利用实时更新背景参数的方法提高模型适应场景变化的能力，并通过实验取得了良好的检测效果。
Abstract:
 The moving target detection systems based on image processing are sensitive to the light conditions of target scene. Present a new single Gauss model moving target detection method based on Kinect depth data,which can enhance robustness of the system in scene collection error. First,median filtering is used to process depth data. Then the single Gauss model is established for the depth data of target scene. The Gauss probability threshold is used to discriminate the collecting data of target scene and background parameters. Moving tar-get detection is achieved after morphological filtering. And real-time updating background parameters is a good way to make the model a-dapt to the change of scene. At last,experiments are performed. The proposed method has achieved well detection results.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(09):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(09):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(09):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(09):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(09):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(09):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(09):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(09):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(09):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(09):47.

更新日期/Last Update: 2015-10-16