Moving Object Detection Using Background Subtraction Algorithms
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Moving Object Detection Using Background Subtraction Algorithms
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Description
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.
Product details
- Type:
- Book
- Binding:
- Paperback
- Edition:
- 1
- Languages:
-
Original:
English
- Number of Pages:
-
64
- Publisher:
-
GRIN Verlag
- Publication Date:
-
2014-06-30
- ISBN / EAN:
-
9783656672678
- Weight:
-
107 g
- Dimensions:
-
15/21/1 cm
- GPSR Manufacturer Reference:
-
[email protected]
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