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Single Moving Object Detection and Tracking based on GMM

机译：基于GMM的单个移动对象检测和跟踪

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摘要

Automatic moving object detection and tracking is very important task in video surveillance applications. In the present work the well known background subtraction model and use of Gaussian Mixture Models (GMM) have been used to implement a robust automated single object tracking system. In this implementation, background subtraction on subtracting consecutive frame-by-frame basis for moving object detection is done. Once the object has been detected it is tracked by employing an efficient GMM technique. This ensures that any change in the pose of the object does not hinder the tracking procedure. The system is capable of handling entry and exit of an object. Such a tracking system is cost effective and can be used as an automated video conferencing system and also has applications like human tracking, vehicles monitoring, and event recognition for video surveillance. The proposed algorithm was tested on standard database on complex environments and the results were satisfactory.
机译：自动移动物体检测和跟踪在视频监控应用中是非常重要的任务。在本工作中，已经使用了众所周知的背景减法模型和高斯混合模型（GMM）的使用来实现强大的自动单对象跟踪系统。在该实现中，完成了对移动对象检测的连续帧逐帧基础进行的背景减法。一旦检测到对象，就通过采用有效的GMM技术来跟踪。这可确保对象的姿势的任何变化都不会阻碍跟踪过程。该系统能够处理对象的条目和退出。这种跟踪系统具有成本效益，并且可以用作自动视频会议系统，并且还具有人类跟踪，车辆监测和用于视频监控的事件识别等应用。在复杂环境的标准数据库上测试了所提出的算法，结果令人满意。

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