An Intelligent Offsite Object Identification and Recognition Video Surveillance System
Ademola S. Olaniyi · 2014
The need for an automated surveillance system in some aspects of our daily life cannot be overemphasized. Existing monitoring video cameras are very cheap, but the human resource to manage them is very expensive and prone to errors. Most surveillance cameras are currently used after an incidence, to run through a set of recorded data to identify a culprit. The need for a continuous monitoring system that can alert users or security officers of an incident in progress is therefore important in today’s risky environment. The methodology uses a combination of temporal differencing and background subtraction method for object detection. It is then followed by a templatematching classification algorithm which uses the object silhouette followed by the contour tracing algorithm. After object detection and classification, we use PCA and feature based technique to identify the human detected. We also carried out performance analysis based on run time, time performance and detection quality of algorithms. The results show that our algorithms give a better performance than existing ones. The system works under existing infrastructures and ISPs without any modification, so that no new popular application on a mobile phone is created.