Criminals often resort to camera tampering to prevent capture of their actions. Many surveillance systems left unattended and videos surveillance system operators lose their concentration after a short period of time. Many important Real-time automated detection of video camera tampering cases is important for timely warning of the operators. Tampering can be defined as deliberate physical actions on a video surveillance camera and is generally done by obstructing the camera view by a foreign object, displacing the camera and changing the focus of the camera lens. In automated camera tamper detection systems, low false alarm rates are important as reliability of these systems is compromised by unnecessary alarms and consequently the operators start ignoring the warnings. We propose adaptive algorithms to detect and identify such cases with low false alarms rates in typical surveillance scenarios where there is significant activity in the scene. We also give brief information about the camera tampering detection algorithms in the literature. In this thesis we compare performance of the proposed algorithms to the algorithms in the literature by experimenting them with a set of test videos.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12610632/index.pdf |
| Date | 01 June 2009 |
| Creators | Saglam, Ali |
| Contributors | Temizel, Alptekin |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
Page generated in 0.0368 seconds