Event Detection Using "Variable Module Graphs" for Home Care Applications
- PDF / 2,709,765 Bytes
- 13 Pages / 600.03 x 792 pts Page_size
- 15 Downloads / 210 Views
Research Article Event Detection Using “Variable Module Graphs” for Home Care Applications Amit Sethi, Mandar Rahurkar, and Thomas S. Huang Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801-2918, USA Received 14 June 2006; Accepted 16 January 2007 Recommended by Francesco G. B. De Natale Technology has reached new heights making sound and video capture devices ubiquitous and affordable. We propose a paradigm to exploit this technology for home care applications especially for surveillance and complex event detection. Complex vision tasks such as event detection in a surveillance video can be divided into subtasks such as human detection, tracking, recognition, and trajectory analysis. The video can be thought of as being composed of various features. These features can be roughly arranged in a hierarchy from low-level features to high-level features. Low-level features include edges and blobs, and high-level features include objects and events. Loosely, the low-level feature extraction is based on signal/image processing techniques, while the high-level feature extraction is based on machine learning techniques. Traditionally, vision systems extract features in a feed-forward manner on the hierarchy, that is, certain modules extract low-level features and other modules make use of these low-level features to extract high-level features. Along with others in the research community, we have worked on this design approach. In this paper, we elaborate on recently introduced V/M graph. We present our work on using this paradigm for developing applications for home care applications. Primary objective is surveillance of location for subject tracking as well as detecting irregular or anomalous behavior. This is done automatically with minimal human involvement, where the system has been trained to raise an alarm when anomalous behavior is detected. Copyright © 2007 Amit Sethi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1.
INTRODUCTION
Even with the US population rapidly aging, a smaller proportion of elderly and disabled people live in nursing homes today compared to 1990. Instead, far more depend on assisted living residences or receive care in their homes [1]. Majority of people who need long-term care still live in nursing homes, however the proportion of nursing home beds declined from 66.7 to 61.4 per 10 000 population. According to the author, these changing trends in the supply of long-term care can be expected to continue because the demand for home- and community-based services is growing. These healthcare services besides being expensive may often be emotionally traumatic for the subject. Large number of these people who live here can perform basic day-today tasks, however need to be under constant supervision in case assistance is required. In this paper, we show how curren
Data Loading...
