Moving Object Detection Using Background Subtraction Algorithms

Master's Thesis from the year 2014 in the subject Computer Science - Theory, grade: 9.2, , language: English, abstract: In this thesis we present an operational computer video system for moving

object detection and tracking . The system captures monocular frames of

background as well as moving object and to detect tracking and identifies

those moving objects. An approach to statistically modeling of moving object

developed using Background Subtraction Algorithms. There are many

methods proposed for Background Subtraction algorithm in past years.

Background subtraction algorithm is widely used for real time moving object

detection in video surveillance system. In this paper we have studied and

implemented different types of methods used for segmentation in Background

subtraction algorithm with static camera. This paper gives good understanding

about procedure to obtain foreground using existing common methods of

Background Subtraction, their complexity, utility and also provide basics which

will useful to improve performance in the future . First, we have explained the

basic steps and procedure used in vision based moving object detection.

Then, we have debriefed the common methods of background subtraction like

Simple method, statistical methods like Mean and Median filter, Frame

Differencing and W4 System method , Running Gaussian Average and

Gaussian Mixture Model and last is Eigenbackground Model. After that we

have implemented all the above techniques on MATLAB software and show

some experimental results for the same and compare them in terms of speed

and complexity criteria. Also we have improved one of the GMM algorithm by

combining it with optical flow method, which is also good method to detect

moving elements.