Moving Object Real-time Detection and Tracking Method Based on Improved Gaussian Mixture Model

被引：0
|
作者：
Zhu, Shanliang [1 ,3 ]
Gao, Xin [1 ]
Wang, Haoyu [1 ]
Xu, Guangwei [1 ]
Xie, Qiuling [2 ]
Yang, Shuguo [3 ]
机构：
[1] Qingdao Univ Sci & Technol, Sch Math & Phys, Res Ctr Math Modeling, Qingdao 266061, Peoples R China
[2] Qingdao Univ Sci & Technol, Finance Off, Qingdao 266061, Peoples R China
[3] Qingdao Univ Sci & Technol, Sch Math & Phys, Inst Intelligence Sci & Data Technol, Qingdao 266061, Peoples R China
来源：
PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS) | 2018年
关键词：
moving object detection; Vibe; Gaussian mixture model; pixel classification;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In order to improve the reliability of moving objects detection and tracking, this paper presents a method for moving object real-time detection and tracking based on Vibe and Gaussian mixture model (GMM). This method uses the "Virtual" background model that is trained by video sequence instead of the first frame image for background modeling. And then the foreground object is extracted based on the pixel classification. Finally, according to the morphological method, the clearer moving targets are conducted to realize the real-time detection and tracking. The experimental results show that, in comparison with the current mainstream background subtraction techniques, our approach effectively works on a wide range of complex scenarios, with faster detection speed and more reliable detection results.
引用
收藏
页码：654 / 658
页数：5
相关论文
共 50 条
  • [41] Foreground Detection of Moving Object Using Gaussian Mixture Model
    Aslam, Nazia
    Sharma, Veena
    [J]. 2017 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), 2017, : 1071 - 1074
  • [42] Real-time Tracking System Combining Mixture of Gaussian Model and CamShift Algorithm
    Li Yuanshuang
    Xie Qinlan
    Yu Gai
    [J]. INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND PATTERN RECOGNITION IN INDUSTRIAL ENGINEERING, 2010, 7820
  • [43] A Robust Real-time Moving Object Tracking Algorithm
    Yang Wenjie
    Li Yun
    [J]. 2009 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-3, 2009, : 1471 - 1475
  • [44] Adaptive real-time tracking algorithm for moving object
    College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    不详
    [J]. Beijing Youdian Daxue Xuebao, 2006, SUPPL. 2 (164-167):
  • [45] Tracking a moving object with real-time obstacle avoidance
    Chen, Chung-Hao
    Cheng, Chang
    Page, David
    Koschan, Andreas
    Abidi, Mongi
    [J]. INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2006, 33 (06): : 460 - 468
  • [46] Real-time tracking of a moving object inside a tube
    Arshak, K.
    Adepoju, F.
    [J]. 2006 25TH INTERNATIONAL CONFERENCE ON MICROELECTRONICS, VOLS 1 AND 2, PROCEEDINGS, 2006, : 249 - +
  • [47] A Shadow Detection Method Based on Improved Gaussian Mixture Model
    Li, Jing
    Wang, Geng
    [J]. 2013 IEEE 4TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC), 2014, : 62 - 65
  • [48] Moving object detection for real-time applications
    Maddalena, Lucia
    Petrosino, Alfredo
    [J]. 14TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, PROCEEDINGS, 2007, : 542 - +
  • [49] Efficient Method for Moving Object Detection in Cluttered Background Using Gaussian Mixture Model
    Yadav, Dileep Kumar
    [J]. 2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2014, : 943 - 948
  • [50] Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis
    Balakrishna, Sivadi
    Thirumaran, Moorthy
    Solanki, Vijender Kumar
    [J]. INGENIERIA SOLIDARIA, 2020, 16 (01):
12345