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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.
