Kamstra, G.J. (2003) Robust motion detection in live outdoor video streams using triplines. Master's Thesis / Essay, Computing Science.
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Abstract
Motion detection is an interesting research subject in computer vision. Multiple approaches are possible. In this thesis we will give a full overview of using triplines in motion detection applications. First we will discuss preprocessing techniques like illumination change compensation. Then we will focus our attention on the actual triplines. Topics here are the placing, the shape, the length and so on. We will discuss several algorithms to use on this triplines (Thresholded Difference, Positive Thresholding, Kolmogorov Smirnov test statistic, Smirnov test statistic, Modified Smirnov test statistic, Approximate Entropy and FFT detect). We will make a comparison between these algorithms on triplines and other ways of motion detection (including non computer vision based). After the discussion on the algorithms, we will define some performance measurements. We will argue the downfall of the count error, which has been used in several papers, and introduce our new and improved count error. The last step in our motion detection is the filtering step. Sequence filtering and average filtering are discussed. Finally we will discuss the impact of frame rate reduction on the performance of our algorithms.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:29 |
| Last Modified: | 15 Feb 2018 07:29 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/8863 |
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