Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Event Detection Using “Variable Module Graphs” for Home Care Applications | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 74243 13 pages doi 2007 74243 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 .
