混合高斯融合三帧差的运动目标检测改进算法

于晓明; 李思颖; 史胜楠

红外技术, 2019, 41 (3): 256, 网络出版: 2019-04-05

混合高斯融合三帧差的运动目标检测改进算法

An Improved Algorithm for Moving Target Detection Using a Gaussian Mixture with Three-frame Difference

论文大纲

于晓明李思颖 ^*史胜楠

作者单位

陕西科技大学电气与信息工程学院, 陕西西安 710021

混合高斯模型三帧差法目标检算法测边缘检测颜色空间 Gaussian mixture model three frame difference method target detection algorithm edge detection color space

摘要

针对混合高斯模型(Gaussian Mixture Model, GMM)无法检测到完整的运动目标, 三帧差法检测目标时对物体速度的敏感, 检测到的物体会出现空洞等缺点, 提出了一种混合高斯融合三帧差法的运动目标检测改进算法。首先, 在运动目标提取过程中, 改进的三帧差法采用动态分割阈值和边缘检测技术, 解决光线突变和边缘不连续问题; 然后引入新的高斯分布自适应选择策略, 以减少处理时间, 提高检测准确性; 最后, 利用改进 HSV(Hue-Saturation-Value)颜色空间来消除阴影区域, 得到一个完整的运动目标。数据实验表明, 该算法在不同场景具有较好的检测能力。

Abstract

For the mixed Gaussian model unable to detect the complete moving target, the three-frame difference method is sensitive to the speed of the object as the target is detected, and defects on the detected object (such as voids) appear. An improved moving target detection algorithm based on a mixed Gaussian fusion three-frame difference method is proposed. First, in the process of moving target extraction, the improved three-detect method uses a dynamic segmentation threshold and an edge detection technology to solve the problem of light mutation and edge discontinuity. Then, a new Gaussian distribution adaptive selection strategy is introduced to reduce processing time and improve detection accuracy. Finally, the improved HSV color space is used to eliminate the shadow area and obtain a complete moving target. Data experiments show that the algorithm has better detection capabilities in various scenarios.

参考文献

[1] XU Y, ZHANG J, GU J, et al. An optimized Vibe target detection algorithm based on gray distribution and Minkowski distance[C]//32nd Youth Academic Annual Conference of Chinese Association of Automation, 2017. DOI: 10.1109/YAC.2017.7967380.

[2] 张荣刚, 顾强. 基于 ViBe的动态目标检测算法优化[J].机械与电子, 2017, 35(4): 21-26.

ZHANG Ronggang, GU Qiang. Optimization of dynamic target detection algorithm based on ViBe[J]. Mechanical and Electronic, 2017, 35(4): 21-26.

[3] HAN X, GAO Y, LU Z, et al. Research on moving object detection algorithm based on improved three frame difference method and optical flow[C]//Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control, 2016. DOI: 10.1109/IMCCC.2015.420.

[4] WEI H, LI J, WU X. Moving object detection algorithm using ViBe combined with frame-difference[J]. Application Research of Computers, 2017, 34(5): 103-107.

[5] 李博川, 丁轲. 结合阴影抑制的混合高斯模型改进算法[J].计算机工程与科学, 2016, 38(3): 556-561.

LI Bochuan, DING Ke. Improved algorithm of hybrid Gaussian model with shadow suppression[J]. Computer Engineering and Science, 2016, 38(3): 556-561.

[6] JIA J, DONG A, Science S O, et al. Moving target detection algorithm based on joint histogram[J]. Computer Engineering & Applications, 2016, 52(5): 199-203.

[7] SHI G, SUO J, LIU C, et al. Moving target detection algorithm in image sequences based on edge detection and frame difference[C]// Information Technology and Mechatronics Engineering Conference of IEEE, 2017: 740-744.

[8] ZHAI J, ZHOU X, WANG C. A moving target detection algorithm based on combination of GMM and LBP texture pattern[C]//Guidance, Navi-gation and Control Conference of IEEE, 2017: 1057-1060.

[9] Prasad K, Sharma R, Wadhwani D. A review on object detection in video processing[J]. International Journal of u- and e- Service, Science and Technology, 2012, 4(5): 15-20.

[10] 尹宏鹏, 陈波, 柴毅, 等. 基于视觉的目标检测与跟踪综述[J].自动化学报, 2016, 42(10): 1466-1489.

YIN Hongpeng, CHEN Bo, CHAI Yi, et al. An overview of visual target detection and tracking[J]. Journal of Automation, 2016, 42(10): 1466-1489.

[11] 王春兰. 智能视频监控系统中运动目标检测方法综述[J]. 自动化与仪器仪表, 2017(3): 1-3.

WANG Chunlan. An overview of moving target detection methods in the intelligent video monitoring system[J]. Automation and Instrumentation, 2017(3): 1-3.

[12] 姬晓飞, 秦宁丽, 刘洋. 多特征的光学遥感图像多目标识别算法[J].智能系统学报, 2016, 11(5): 655-662.

JI Xiaofei, QIN Ningli, LIU Yang. Multi-feature optical remote sensing image like multi-target recognition algorithm[J]. Journal of Intelligent Systems, 2016, 11(5): 655-662.

[13] 赵燕熙, 尚振宏, 刘辉, 等. 动态背景下空时特性均显著的运动目标检测[J].计算机工程与应用, 2017, 53(5): 170-175.

ZHAO Yanxi, SAHNG Zhenhong, LIU Hui, et al. Dynamic target detection in the dynamic background of space-time[J]. Computer Engineering and Application, 2017, 53(5): 170-175.

[14] 王忠华, 王超. 联合帧间差分和边缘检测的运动目标检测算法[J].南昌大学学报: 理科版, 2017, 41(1): 42-46.

WANG Zhonghua, WANG Chao. Moving target detection algorithm for combination frame difference and edge detection[J]. Journal of Nanchang University: Science Edition, 2017, 41(1): 42-46.

于晓明, 李思颖, 史胜楠. 混合高斯融合三帧差的运动目标检测改进算法[J]. 红外技术, 2019, 41(3): 256. YU Xiaoming, LI Siying, SHI Shengnan. An Improved Algorithm for Moving Target Detection Using a Gaussian Mixture with Three-frame Difference[J]. Infrared Technology, 2019, 41(3): 256.

混合高斯融合三帧差的运动目标检测改进算法

关于本站 Cookie 的使用提示

全站搜索

混合高斯融合三帧差的运动目标检测改进算法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索