Integrating RFID on event-based hemispheric imaging for internet of things assistive applications
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
Unusual activity detection for video surveillance
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Occlusion handling based on sub-blobbing in automated video surveillance system
Proceedings of The Fourth International C* Conference on Computer Science and Software Engineering
Video semantic concept detection using ontology
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
A user-centric control and navigation for augmented virtual environment surveillance application
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
Locating emergencies in a campus using wi-fi access point association data
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Fast background subtraction using static and dynamic gates
Artificial Intelligence Review
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Surveillance cameras are inexpensive and everywhere these days but the manpower required to monitor and analyze them is expensive. Consequently the videos from these cameras are usually monitored sparingly or not at all; they are often used merely as archive, to refer back to once an incident is known to have taken place. Surveillance cameras can be a far more useful tool if instead of passively recording footage, they can be used to detect events requiring attention as they happen, and take action in real time. This is the goal of automated visual surveillance: to obtain a description of what is happening in a monitored area, and then to take appropriate action based on that interpretation. Video surveillance for humans is one of the most active research topics in computer vision. It has a wide spectrum of promising homeland security applications. Video management and interpretation systems have become quite capable in recent years. This paper looks into how hardware and software can be put together to solve surveillance problems in an age of increased concern with public safety and security. In general, the framework of a video surveillance system includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, behavior understanding and description, and fusion of information from multiple cameras. Despite recent progress in computer vision and other related areas, there are still major technical challenges to be overcome before reliable automated video surveillance can be realized. This paper reviews developments and general strategies of stages involved in video surveillance, and analyzes the feasibility and challenges for combining motion analysis, behavior analysis, and standoff biometrics for identification of known suspects, anomaly detection, and behavior understanding.