This article provides an overview and critical analysis of computer vision algorithms used in the construction of the modules responsible for recognition and identification of objects which are elements of an intelligent monitoring system. We investigate state-of-the-art methods with an enough high potential to be implemented in a practical realization of such a system. The article describes three main elements of modern surveillance system, namely an adaptive background model, object extraction and tracking. Finally, we describe several recent benchmark datasets that can be used to test real systems.
One of the most crucial aspects to be taken into consideration during the development of visual surveillance system is the need to inform the human operator about any unusual situation happening. The paper presents an idea of real-time video stream analysis which leads to the detection and tracking of suspicious objects that have been left unattended. The mathematical principles related to background model creation and detection tuning are included. Developed algorithm has been implemented as a working model involving OpenCV library and tested on benchmark data taken from real visual surveillance system.
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