“SmartMonitor” is an intelligent security system based on image analysis that combines the advantages of alarm, video surveillance and home automation systems. The system is a complete solution that automatically reacts to every learned situation in a pre-specified way and has various applications, e.g., home and surrounding protection against unauthorized intrusion, crime detection or supervision over ill persons. The software is based on well-known and proven methods and algorithms for visual content analysis (VCA) that were appropriately modified and adopted to fit specific needs and create a video processing model which consists of foreground region detection and localization, candidate object extraction, object classification and tracking. In this paper, the “SmartMonitor” system is presented along with its architecture, employed methods and algorithms, and object analysis approach. Some experimental results on system operation are also provided. In the paper, focus is put on one of the aforementioned functionalities of the system, namely supervision over ill persons.

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.

This paper presents the idea of using simple shape features for action recognition based on binary silhouettes. Shape features are analysed as they change over time within an action sequence. It is shown that basic shape characteristics can discriminate between short, primitive actions performed by a single person. The proposed approach is tested on the Weizmann database using a various number of classes. Binary foreground masks (silhouettes) are replaced with convex hulls, which highlights some shape characteristics. Centroid locations are combined with some other simple shape descriptors. Each action sequence is represented using a vector with shape features and Discrete Fourier Transform. Classification is based on leave-one-sequence-out approach and employs Euclidean distance, correlation coefficient or C1 correlation. A list of processing steps for action recognition is explained and followed by some experiments that yielded accuracy exceeding 90%. The idea behind the presented approach is to develop a solution for action recognition that could be applied in a kind of human activity recognition system associated with the Ambient Assisted Living concept, helping adults increasing their activity levels by monitoring them during exercises.