Sebastian Lühr (2006) Techniques for the Discovery of Anomalous Human Behaviour in Intelligent Environments. PhD Thesis, Department of Computing, Curtin University of Technology

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Abstract: Motivated by a desire to create smart homes that will enable the elderly to maintain their independence for as long as possible, this thesis presents techniques for detecting abnormality in human activity observed in both laboratory and real world smart environments.

The use of stochastic models as tools for learning models of normality, with which incoming observational data from a visual tracking system can be examined, is investigated. In particular, the Hierarchical Hidden Markov Model (HHMM) is applied to the training of multi-level models of behaviour to show that the hierarchical structure of the model allows for a more expressive representation of human behaviour than is possible using flat models. The usefulness of modelling duration in models of human activity is then investigated by comparing the classification and abnormality detection performance of the Hidden Markov Model (HMM) against that of the Explicit State Duration HMM (ESD-HMM). The data sets used differ primarily in the duration of activities rather than in the ordering of the events. An extension of the ESD-HMM where the state transition times are inferred from an observation signal that has been augmented with pressure mat sensor data is then introduced. Work into this area is then concluded with results from experimentation on real world data.

A data mining technique that employs Intertransaction Association Rule (IAR) mining to discover new and changing human behaviours is then presented. The Frequent Pattern Tree (FP-Tree) and the Frequent Pattern Growth (FP-Growth) algorithm are extended for IAR mining. The resulting data structure and mining algorithm, dubbed the Extended FP-Tree (EFP-Tree) and Extended FP-Growth (EFP-Growth) respectively, are benchmarked against the First Intra Then Inter (FITI) algorithm, the existing state of the art algorithm for IAR mining. Results demonstrating that the EFP-Growth algorithm is an order of magnitude computationally more efficient than FITI are presented and discussed. The viability of emergent IAR mining as a technique for identifying unexpected behaviours in a smart home environment is affirmed with a discussion of observations made mining emergent behaviours from sensor event data recorded in the homes of two real world subjects.

Finally, a novel visual interface that enables emergent behaviours to be examined in the context of the original data is introduced. Mapping emergent IARs back into the original data space, the interface is demonstrated to allow greater insight to be gained in significantly less time than is possible by manual inspection of the sensor event log data.

Techniques for the Discovery of Anomalous Human Behaviour in Intelligent Environments

Sebastian Lühr (2001) Providing Portable Hand-Held Access to a Wireless Video Surveillance System. Honours Dissertation, Department of Computing, Curtin University of Technology

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Abstract: Research into computer based video surveillance has led to sophisticated surveillance systems capable of monitoring and classifying human behaviour and featuring advanced scene understanding. Many such systems stream raw video data to centralised processing clusters for analysis.

Trends indicate that, with the increasing processing capabilities of embedded hardware, low level video processing will migrate from centralised configurations to the sensors themselves. It is expected that these ‘intelligent’ devices will be equipped with wireless interfaces so as to allow for true ubiquitous installation.

This work discusses how existing technologies have been combined to build a foundation for the provision of portable hand-held access to current and next generation wireless video surveillance systems. The design and implementation of an initial server and client software infrastructure that provides camera discovery and video streaming is discussed.