Event Detection Using "Variable Module Graphs" for Home Care Applications. (19th April 2007)

• Record Type: Journal Article
• Authors: Sethi Sethi, Amit Amit; Rahurkar Rahurkar, Mandar Mandar; Huang Huang, Thomas S. Thomas S.
• Other Names: De Natale De Natale Francesco G. B. Francesco G. B. Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2007-04-19
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/74243
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : 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.