Adaptive camera tamper detection for video surveillance

Download
2009
Sağlam, Ali
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.

Suggestions

Real-time Adaptive Camera Tamper Detection for Video Surveillance
SAĞLAM, Ali; Temizel, Alptekin (2009-09-04)
Criminals often resort to camera tampering to prevent capture of their actions. Real-time automated detection of video camera tampering cases is important for timely warning of the operators. Tampering 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 ope...
Automated crowd behavior analysis for video surveillance applications
Güler, Püren; Temizel, Alptekin; Taşkaya Temizel, Tuğba; Department of Information Systems (2012)
Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups; people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behavi...
Semantic Annotation of Surveillance Videos for Abnormal Crowd Behaviour Search and Analysis
Sah, Melike; Direkoglu, Cem (2017-09-01)
Monitoring videos captured by surveillance cameras is a very difficult and time consuming task. There is a need for automated analysis using computer vision methods in order to recognize abnormal human behaviors and assist authorities. On the other hand, crowd (group of people) behavior analysis is a new direction of research, which can be utilized for automatic detection of panic in crowds. Once, videos are processed using computer vision technologies, another problem is how this data is indexed for search...
A continuous object tracking system with stationary and moving camera modes
Emeksiz, Deniz; Temizel, Alptekin (2012-09-27)
Automatic detection and tracking of objects get more important with the increasing number of surveillance cameras and mobile platforms having cameras. Tracking systems are either designed with stationary camera or designed to work in moving camera. When the camera is stationary, correspondence based tracking with background subtraction has a number of benefits such as enabling automatic detection of new objects in the scene and better tracking accuracy. On the other hand, mean shift is a histogram-based tra...
Clustering of Local Behaviour in Crowd Videos
Öngün, Cihan; Temizel, Alptekin; Taşkaya Temizel, Tuğba (2014-04-25)
Surveillance cameras are playing more important role in our daily life with the increasing number of human population and surveillance cameras. While there are a myriad of methods for video analysis, they are generally designed for low-density areas. Running of these algorithms in crowded areas would not give expected results and results in high number of false alarms giving rise to a need for different approaches for crowded area surveillance. Due to occlusions and images of individuals having a low resolu...
Citation Formats
A. Sağlam, “Adaptive camera tamper detection for video surveillance,” M.S. - Master of Science, Middle East Technical University, 2009.