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SmartMonitor is an innovative surveillance system based on video content analysis. It is a modular solution that can work in several predefined scenarios mainly concerned with home/surrounding protection against unauthorized intrusion, supervision over ill person and crime detection. Each scenario is associated with several actions and conditions, which imply the utilization of algorithms with various input parameters. In this paper, focus is put on the analysis of foreground object patterns for the purposes of event recognition, as well as the experimental investigation of selected methods and algorithms which were developed and employed for the SmartMonitor system prototype. The prototype performs three main tasks: detection and localization of foreground regions using adaptive background modelling based on Gaussian Mixture Models, candidate objects extraction and classification using Haar and HOG descriptors, and tracking using Mean-Shift algorithm. The main goal of the work described here is to match system parameters with each scenario to provide the highest effectiveness and to decrease the number of false alarms.

Application of foreground object patterns analysis for event detection in an innovative video surveillance system