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Intelligent monitoring for people assistance and safety
Author(s) -
MartínezTomás Rafael,
FernándezCaballero Antonio,
Ferrández José Manuel
Publication year - 2014
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12016
Subject(s) - computer science , process (computing) , telecommunications , computer security , data science , real time computing , operating system
This expert systems special issue on ‘Intelligent Monitoring for People Assistance and Safety’ contains the revised and extended best papers dealing with different issues concerning people assistance and safety through intelligent monitoring and activity interpretation, presented at ‘IWINAC 2011: the fourth International Work-Conference on the Interplay between Natural and Artificial Computation’. People assistance and safety is a hot topic and of crucial importance in indoor environments such as homes, offices, hospitals and schools as well as in outdoor areas. Environments are increasingly well equipped with multiple sensing technologies that can monitor simple and complex activities and behaviours (Gascueña and Fernández-Caballero, 2011). Intelligent monitoring implies not only the analysis of the data captured from the various sensors but also their interpretation from the detection of the presence of certain events or actions previously defined (Rivas, Martínez-Tomás and Fernández-Caballero, 2011). From a historical perspective, it is acknowledged that the evolution of monitoring systems has gone through three generations. In the first generation (1960–1980), closedcircuit television analogue systems were used, which consisted of several cameras connected to a series of monitors. These systems do not process information and require a human operator to be permanently concentrated on analysing the situations observed on the monitors. However, in the second generation (1990–2000), advances attained in digital video communication (e.g. digital compression, bandwidth reduction and robust transmission) were used to increase the efficiency of monitoring systems: closed-circuit television systems were combined with computer vision technology to process images automatically, in order to be proactive in the detection of alarm events during recording. These semiautomatic systems required a robust tracking and detection algorithm for behaviour analysis. Whereas these systems represented a clear improvement with respect to first generation systems by reducing the dependency on human operators to detect anomalous situations, their algorithms and techniques were responsible for triggering a high number of false positives. In the third generation (2000-today), a series of heterogeneous sensors (e.g. fixed cameras, pan-tilt-zoom (PTZ) cameras, audio sensors and RFID tags (radio-frequency identification) will be geographically distributed throughout the scenario to be observed. From the image processing point of view, these systems are based on distributed processing capabilities and the use of embedded signal processing devices to gain distributed scalability and robustness. The main problems that need to be solved in third generation systems are the integration of data obtained from different sensors, establishing a correspondence of the signals in time and space and coordinating and distributing the processing task and video communication.