Multiview gait biometrics for human identity recognition

We propose a novel multiview fusion scheme for recognizing human identity based on gait biometric data. The gait biometric data is acquired from video surveillance datasets from multiple cameras. Experiments on publicly available CASIA dataset show the potential of proposed scheme based on fusion towards development and implementation of automatic identity recognition systems.

Enhancing human action recognition with region proposals

Deep convolutional network models have dominated recent work in human action recognition as well as image classification. However, these methods are often unduly influenced by the image background, learning and exploiting the presence of cues in typical computer vision datasets. For unbiased robotics applications, the degree of variation and novelty in action backgrounds is far greater than in computer vision datasets. To address this challenge, we propose an “action region proposal” method that, informed by optical flow, extracts image regions likely to contain actions for input into the network both during training and testing. In a range of experiments, we demonstrate that manually segmenting the background is not enough; but through active action region proposals during training and testing, state-of-the-art or better performance can be achieved on individual spatial and temporal video components. Finally, we show by focusing attention through action region proposals, we can further improve upon the existing state-of-the-art in spatio-temporally fused action recognition performance.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Conscious and unconscious processes in human desire

Elaborated Intrusion theory (Kavanagh, Andrade & May 2005) distinguishes between unconscious, associative processes as the precursors of desire, and controlled processes of cognitive elaboration that lead to conscious sensory images of the target of desire and associated affect. We argue that these mental images play a key role in motivating human behavior. Consciousness is functional in that it allows competing goals to be compared and evaluated. The role of effortful cognitive processes in desire helps to explain the different time courses of craving and physiological withdrawal.

The relationship between unsafe working conditions and workers’ behavior and their impacts on injury severity in the U.S.

Unsafe acts of workers (e.g. misjudgment, inappropriate operation) become the major root causes of construction accidents when they are combined with unsafe working conditions (e.g. working surface conditions, weather) on a construction site. The overarching goal of the research presented in this paper is to explore ways to prevent unsafe acts of workers and reduce the likelihood of construction accidents occurring. The study specifically aims to (1) understand the relationships between human behavior related and working condition related risk factors, (2) identify the significant behavior and condition factors and their impacts on accident types (e.g. struck by/against, caught in/between, falling, shock, inhalation/ingestion/absorption, respiratory failure) and injury severity (e.g. fatality, hospitalized, non-hospitalized), and (3) analyze the fundamental accident-injury relationship on how each accident type contributes to the injury severity. The study reviewed 9,358 accidents which occurred in the U.S. construction industry between 2002 and 2011. The large number of accident samples supported reliable statistical analyses. The analysis identified a total of 17 significant correlations between behavior and condition factors and distinguished key risk factors that highly impacted on the determination of accident types and injury severity. The research outcomes will assist safety managers to control specific unsafe acts of workers by eliminating the associated unsafe working conditions and vice versa. They also can prioritize risk factors and pay more attention to controlling them in order to achieve a safer working environment.

Incorporating human-factors in car-following models : a review of recent developments and research needs

Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists. While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process. This paper provides a comprehensive review of these two research streams. It is necessary to consider human-factors in {CF} modeling for a more realistic representation of {CF} behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis. While there are some excellent reviews of {CF} models available in the literature, none of these specifically focuses on the human factors in these models. This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view. In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Psychosocial predictors of the use of enhanced podcasting in student learning

The current study examined the influence of psychosocial constructs, from a theory of planned behavior (TPB) perspective, to predict university students’ (N = 159) use of a newly offered on-line learning tool, enhanced podcasts. Pre-semester, students completed questionnaires assessing the TPB predictors (attitude, subjective norm, perceived behavioral control) related to intended enhanced podcast use until the middle of semester. Mid-semester, students completed similar items relating to podcast use until the end of semester. Self-report measures of podcast use were obtained at the middle and end of semester. At both time points, students’ attitudes predicted their intentions and, at the initial time point, subjective norm also predicted intended podcast use. An examination of the beliefs underlying attitudes, the only construct to predict intentions at both time points, revealed differences between those students higher, rather than lower on intentions to use the podcasts, especially for the perceived educational benefits of podcast use later in the semester. Intentions to use enhanced podcasting only predicted self-reported use in the second half of the semester. Overall, this study identified some of the determinants which should be considered by those aiming to encourage student use of novel on-line educational tools.

Surviving the Titanic disaster : economic, natural and social determinants

The sinking of the Titanic in April 1912 took the lives of 68 percent of the people aboard. Who survived? It was women and children who had a higher probability of being saved, not men. Likewise, people traveling in first class had a better chance of survival than those in second and third class. British passengers were more likely to perish than members of other nations. This extreme event represents a rare case of a well-documented life and death situation where social norms were enforced. This paper shows that economic analysis can account for human behavior in such situations.

Predicting adolescents’ use of social networking sites from an extended theory of planned behaviour perspective

The popularity of social networking sites (SNSs) among adolescents has grown exponentially, with little accompanying research to understand the influences on adolescent engagement with this technology. The current study tested the validity of an extended theory of planned behaviour model (TPB), incorporating the additions of group norm and self-esteem influences, to predict frequent SNS use. Adolescents (N = 160) completed measures assessing the standard TPB constructs of attitude, subjective norm, perceived behavioural control (PBC), and intention, as well as group norm and self-esteem. One week later, participants reported their SNS use during the previous week. Support was found for the standard TPB variables of attitude and PBC, as well as group norm, in predicting intentions to use SNS frequently, with intention, in turn, predicting behaviour. These findings provide an understanding of the factors influencing frequent engagement in what is emerging as a primary tool for adolescent socialisation.

Interaction of natural survival instincts and internalized social norms exploring the Titanic and Lusitania disasters

To understand human behavior, it is important to know under what conditions people deviate from selfish rationality. This study explores the interaction of natural survival instincts and internalized social norms using data on the sinking of the Titanic and the Lusitania. We show that time pressure appears to be crucial when explaining behavior under extreme conditions of life and death. Even though the two vessels and the composition of their passengers were quite similar, the behavior of the individuals on board was dramatically different. On the Lusitania, selfish behavior dominated (which corresponds to the classical homo oeconomicus); on the Titanic, social norms and social status (class) dominated, which contradicts standard economics. This difference could be attributed to the fact that the Lusitania sank in 18 minutes, creating a situation in which the short-run flight impulse dominates behavior. On the slowly sinking Titanic (2 hours, 40 minutes), there was time for socially determined behavioral patterns to re-emerge. To our knowledge, this is the first time that these shipping disasters have been analyzed in a comparative manner with advanced statistical (econometric) techniques using individual data of the passengers and crew. Knowing human behavior under extreme conditions allows us to gain insights about how varied human behavior can be depending on differing external conditions.

Keeping in constant touch : the predictors of young Australians’ mobile phone involvement

Little is known about the psychological underpinnings of young people’s mobile phone behaviour. In the present research, 292 young Australians, aged 16–24 years, completed an online survey assessing the effects of self-identity, in-group norm, the need to belong, and self-esteem on their frequency of mobile phone use and mobile phone involvement, conceptualised as people’s degree of cognitive and behavioural association with their mobile phone. Structural equation modelling revealed that age (younger) and self-identity significantly predicted the frequency of mobile phone use. In contrast, age (younger), gender (female), self-identity and in-group norm predicted young people’s mobile phone involvement. Neither self-esteem nor the need to belong significantly predicted mobile phone behaviour. The present study contributes to our understanding of this phenomenon and provides an indication of the characteristics of young people who may become highly involved with their mobile phone.

Extending Alexander’s ecological dominance-social competition (EDSC) evolutional model

Alexander’s Ecological Dominance and Social Competition (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality (Alexander, 1990; Finn, Geary & Ward, 2005; Irons, 2005). His model provides a basis for explaining the evolution of human socio-cognitive abilities. Our paper examines the extension of Alexander’s model to incorporate the human trait of information behavior in synergy with ecological dominance and social competition as a human socio-cognitive competence. This paper discusses the various interdisciplinary perspectives exploring how evolution has shaped information behavior and why information behavior is emerging as an important human socio-cognitive competence. This paper outlines these issues, including the extension of Spink and Currier’s (2006a,b) evolution of information behavior model towards a more integrated understanding of how information behaviors have evolved (Spink & Cole, 2006).