Smart Camera Based Monitoring System and Its Application to Assisted Living

Dept. of Graphical-Interactive Syst., Univ. of Tubingen, Tubingen
Proceedings of the IEEE (Impact Factor: 4.93). 11/2008; 96(10):1698 - 1714. DOI: 10.1109/JPROC.2008.928765
Source: IEEE Xplore


Western societies are aging rapidly. An automated 24/7 surveillance to ensure safety of the elderly while respecting privacy becomes a major challenge. At the same time this is representative of novel and emerging video surveillance applications discovered lately besides the classic surveillance protection applications in airports, government buildings, and industrial plants. Three problems of current surveillance systems are identified. A distributed and automated smart camera based approach is proposed that addresses these problems. The proposed system's goal set is to analyze the real world and reflect all relevant-and only relevant-information live in an integrated virtual counterpart for visualization. It covers georeferenced person tracking and activity recognition (falling person detection). A prototype system installed in a home for assisted living has been running 24/7 for several months now and shows quite promising performance.

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    • "The computational power has been increased while, at the same time, costs have been reduced. Furthermore , computer vision advances have given video cameras the ability of 'seeing', becoming smart cameras (Fleck & Strasser, 2008). This has enabled the development of vision-based intelligent monitoring systems that are able to automatically extract useful information from visual data to analyse actions, activities and behaviours (Chaaraoui et al., 2012b), both for individuals and crowds, monitoring, recording and indexing video bitstreams (Tian et al., 2008). "
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    ABSTRACT: Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual’s privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users’ acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people’s privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.
    Expert Systems with Applications 06/2015; DOI:10.1016/j.eswa.2015.01.041 · 2.24 Impact Factor
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    • "In fact, roughly 20% of the world's population will be age 60 or older by 2050 [3],[17]. Approximately 20% of the people on earth will be aged 60 or above by 2050 [17]. Increase in age brings several contests to the elders due to their cognitive decay, age-related diseases, along with limitations on the physical activities like vision and hearing. "
    Dataset: UTM Paper

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    • "For example, The Smart Condo by Boers et al. [5] uses a 3D virtual environment to visualize information from an inhouse sensor network to visualize the accurate location of the inhabitant and also some activity such as sitting down. In [6], the 3D virtual environment representation is used with smart cameras to display the location and falling of an elderly patient, hiding other information unless the patient's well being is compromised and the situation dictates the need for a regular video feed. As in previous examples, the 3D virtual environment is flexible in determining which information is displayed and to whom. "
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    ABSTRACT: In this paper we present our method of detecting elderly patient's activities using a low-cost mobile sensor network. The activities are visualized using a 3D virtual environment. The sensor network consists of two wearable accelerometer/gyro sensors and 4-6 proximity sensors. A pattern recognition system detects the activities and a 3D virtual environment platform visualizes 19 actions and the approximate location of the patient, who is represented as a humanoid avatar. We evaluate our system with healthcare professionals using the focus group interview method and present the results as guidelines for using 3D virtual environments with elderly patient monitoring.
    7th International Conference on Next Generation Mobile Apps, Services and Technologies; 09/2013
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