A computational framework for unsupervised analysis of everyday human activities

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/24765

Title: A computational framework for unsupervised analysis of everyday human activities
Author: Hamid, Muhammad Raffay
Abstract: In order to make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. This presents us with the challenge of formalizing computational models of everyday human activities. For a majority of environments, the structure of the in situ activities is generally not known a priori. This thesis therefore investigates knowledge representations and manipulation techniques that can facilitate learning of such everyday human activities in a minimally supervised manner. A key step towards this end is finding appropriate representations for human activities. We posit that if we chose to describe activities as finite sequences of an appropriate set of events, then the global structure of these activities can be uniquely encoded using their local event sub-sequences. With this perspective at hand, we particularly investigate representations that characterize activities in terms of their fixed and variable length event subsequences. We comparatively analyze these representations in terms of their representational scope, feature cardinality and noise sensitivity. Exploiting such representations, we propose a computational framework to discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding concise characterizations of these discovered activity-classes, both from a holistic as well as a by-parts perspective. Using such characterizations, we present an incremental method to classify a new activity instance to one of the discovered activity-classes, and to automatically detect if it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our framework in a variety of everyday environments.
Type: Dissertation
URI: http://hdl.handle.net/1853/24765
Date: 2008-07-07
Publisher: Georgia Institute of Technology
Subject: Computational perception
Activity recognition
Anomaly detection
Artificial intelligence
Behavior modeling
Scene understanding
Automatic data collection systems
Ubiquitous computing
Optical detectors
Human-computer interaction
Expert systems (Computer science)
Computer vision
Department: Computing
Advisor: Committee Chair: Aaron Bobick; Committee Member: Charles Isbell; Committee Member: David Hogg; Committee Member: Irfan Essa; Committee Member: James Rehg
Degree: Ph.D.

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