Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

Supporting collaboration on very large-scale interactive wall surfaces

In this paper we describe CubIT, a multi-user presentation and collaboration system installed at the Queensland University of Technology’s (QUT) Cube facility. The ‘Cube’ is an interactive visualisation facility made up of five very large-scale interactive multi-panel wall displays, each consisting of up to twelve 55-inch multi-touch screens (48 screens in total) and massive projected display screens situated above the display panels. The paper outlines the unique design challenges, features, implementation and evaluation of CubIT. The system was built to make the Cube facility accessible to QUT’s academic and student population. CubIT enables users to easily upload and share their own media content, and allows multiple users to simultaneously interact with the Cube’s wall displays. The features of CubIT were implemented via three user interfaces, a multi-touch interface working on the wall displays, a mobile phone and tablet application and a web-based content management system. Each of these interfaces plays a different role and offers different interaction mechanisms. Together they support a wide range of collaborative features including multi-user shared workspaces, drag and drop upload and sharing between users, session management and dynamic state control between different parts of the system. The results of our evaluation study showed that CubIT was successfully used for a variety of tasks, and highlighted challenges with regards to user expectations regarding functionality as well as issues arising from public use.

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

Deterrence of drug driving : the impact of the ACT drug driving legislation and detection techniques

Overarching Research Questions Are ACT motorists aware of roadside saliva based drug testing operations? What is the perceived deterrent impact of the operations? What factors are predictive of future intentions to drug drive? What are the differences between key subgroups

Identifying product families using data mining techniques in manufacturing paradigm

Identifying product families has been considered as an effective way to accommodate the increasing product varieties across the diverse market niches. In this paper, we propose a novel framework to identifying product families by using a similarity measure for a common product design data BOM (Bill of Materials) based on data mining techniques such as frequent mining and clus-tering. For calculating the similarity between BOMs, a novel Extended Augmented Adjacency Matrix (EAAM) representation is introduced that consists of information not only of the content and topology but also of the fre-quent structural dependency among the various parts of a product design. These EAAM representations of BOMs are compared to calculate the similarity between products and used as a clustering input to group the product fami-lies. When applied on a real-life manufacturing data, the proposed framework outperforms a current baseline that uses orthogonal Procrustes for grouping product families.

'Women on a Wiki’ : the role of interactive websites in reflective learning practices for undergraduate women’s health students

Aim Our pedagogical research addressed the following research questions: 1) Can shared ‘cyber spaces’, such as a ‘wiki’, be occupied by undergraduate women’s health students to improve their critical thinking skills? 2) What are the learning processes via which this occurs? 3) What are the implications of this assessment trial for achieving learning objectives and outcomes in future public health undergraduate courses? Methods The students contributed written, critical reflections (approximately 250 words) to the Wiki each week following the lecture. Students reflected on a range of topics including the portrayal of women in the media, femininity, gender inequality, child bearing and rearing, domestic violence, mental health, Indigenous women, older women, and LGBTIQ communities. Their entries were anonymous, but visible to their peers. Each wiki entry contained a ‘discussion tab’ wherein online conversations were initiated. We used a social constructivist approach to grounded theory to analyse the 480 entries posted over the semester. (http://pub336womenshealth.wikispaces.com/) Results The social constructivist approach initiated by Vygotsky (1978) and further developed by Jonasson (1994) was used to analyse the students’ contributions in relation to four key thematic outcomes including: 1) Complexities in representations across contexts; 2) Critical evaluation in real world scenarios; 3) Reflective practice based on experience, and; 4) Collaborative co-construction of knowledge. Both text and image/visual contributions are provided as examples within each of these learning processes. A theoretical model depicting the interactive learning processes that occurred via discussion of the textual and visual stimulus is presented.

Best practice in peer-led curriculum content : informing an interactive program to improve passenger safety among high school seniors

Despite ongoing improvements in behaviour change strategies, licensing models and road law enforcement measures young drivers remain significantly over-represented in fatal and non-fatal road related crashes. This paper focuses on the safety of those approaching driving age and identifies both high priority road safety messages and relevant peer-led strategies to guide the development school programs. It summarises the review in a program logic model built around the messages and identified curriculum elements, as they may be best operationalised within the licensing and school contexts in Victoria. This paper summarises a review of common deliberate risk-taking and non-deliberate unsafe driving behaviours among novice drivers, highlighting risks associated with speeding, driving while fatigued, driving while impaired and carrying passengers. Common beliefs of young people that predict risky driving were reviewed, particularly with consideration of those beliefs that can be operationalised in a behaviour change school program. Key components of adolescent risk behaviour change programs were also reviewed, which identified a number of strategies for incorporation in a school based behaviour change program, including: a well-structured theoretical design and delivery, thoughtfully considered peer-selected processes, adequate training and supervision of peer facilitators, a process for monitoring and sustainability, and interactive delivery and participant discussions. The research base is then summarised in a program logic model with further discussion about the quality of the current state of knowledge of evaluation of behaviour change programs and the need for considerable development in program evaluation.

Understanding welding practices on shipyards : an ethnographic study for designing an interactive robot welder

The aim of this ethnographic study was to understand welding practices in shipyard environments with the purpose of designing an interactive welding robot that can help workers with their daily job. The robot is meant to be deployed for automatic welding on jack-up rig structures. The design of the robot turns out to be a challenging task due to several problematic working conditions on the shipyard, such as dust, irregular floor, high temperature, wind variations, elevated working platforms, narrow spaces, and circular welding paths requiring a robotic arm with more than 6 degrees of freedom. Additionally, the environment is very noisy and the workers – mostly foreigners – have a very basic level of English. These two issues need to be taken into account when designing the interactive user interface for the robot. Ideally, the communication flow between the two parties involved should be as frictionless as possible. The paper presents the results of our field observations and welders’ interviews, as well as our robot design recommendation for the next project stage.

J-Circos : an interactive Circos plotter

Circos plots are graphical outputs that display three dimensional chromosomal interactions and fusion transcripts. However, the Circos plot tool is not an interactive visualization tool, but rather a figure generator. For example, it does not enable data to be added dynamically, nor does it provide information for specific data points interactively. Recently, an R-based Circos tool (RCircos) has been developed to integrate Circos to R, but similarly, Rcircos can only be used to generate plots. Thus, we have developed a Circos plot tool (J-Circos) that is an interactive visualization tool that can plot Circos figures, as well as being able to dynamically add data to the figure, and providing information for specific data points using mouse hover display and zoom in/out functions. J-Circos uses the Java computer language to enable it to be used on most operating systems (Windows, MacOS, Linux). Users can input data into JCircos using flat data formats, as well as from the GUI. J-Circos will enable biologists to better study more complex chromosomal interactions and fusion transcripts that are otherwise difficult to visualize from next-generation sequencing data.