Introduction : creative industries and cities

The 2000s were marked by a resurgence of interest in creativity and cities. If the rapid global proliferation of the Internet and digital media technologies in the 1990s had set off enthusiasm for a post-industrial ‘new economy’, where the significance of location would be in decline, the 2000s saw an energetic search by artists, entrepreneurs, investors, policy-makers, journalists and many others to uncover the well-springs of creativity and its relationship to place (Flew 2012a). This chapter begins with a discussion of the discourses or ‘scripts’ that have emerged to try and conceptualise the relationship between creativity and cities, notably theories of creative clusters, creative cities and creative class theories. Such work can be seen as representing a growth in the field of cultural economic geography although – as is noted in the chapter – it possesses some significant gaps. Among the issues that are drawn out in this book, and discussed in this chapter, are: the need to move beyond ‘imagined geographies’ of creative inner cities and come to terms with empirical evidence that suggests significant concentrations of the creative workforce in suburbs and regional cities; the relevance of urban cultural policy as a variable in the rise of cities as creative hubs or, in a different model, media capitals; and the challenges of bringing together cultural research with economic discourses in ways that get beyond caricatured representations of the ‘other’, as found, for instance, in some of the most influential framings of the concept of neo-liberalism.

Dynamic load sharing in heavy vehicle suspensions - further analysis of the equalisation effects of larger air lines

This paper provides details on comparative testing of axle-to-chassis forces of two heavy vehicles (HVs) based on an experimental programme carried out in 2007. Dynamic forces at the air springs were measured against speed and roughness values for the test roads used. One goal of that programme was to determine whether dynamic axle-to-chassis forces could be reduced by using larger-than-standard diameter longitudinal air lines. This paper presents a portion of the methodology, analysis and results from that programme. Two analytical techniques and their results are presented. The first uses correlation coefficients of the forces between air springs and the second is a student’s t-test. These were used to determine the causality surrounding improved dynamic load sharing between heavy vehicle air springs with larger air lines installed longitudinally compared with the standard sized air lines installed on the majority of air-sprung heavy vehicles.

Second-order inelastic analysis of composite framed structures based on the refined plastic hinge method

Composite steel-concrete structures experience non-linear effects which arise from both instability-related geometric non-linearity and from material non-linearity in all of their component members. Because of this, conventional design procedures cannot capture the true behaviour of a composite frame throughout its full loading range, and so a procedure to account for those non-linearities is much needed. This paper therefore presents a numerical procedure capable of addressing geometric and material non-linearities at the strength limit state based on the refined plastic hinge method. Different material non-linearity for different composite structural components such as T-beams, concrete-filled tubular (CFT) and steel-encased reinforced concrete (SRC) sections can be treated using a routine numerical procedure for their section properties in this plastic hinge approach. Simple and conservative initial and full yield surfaces for general composite sections are proposed in this paper. The refined plastic hinge approach models springs at the ends of the element which are activated when the surface defining the interaction of bending and axial force at first yield is reached; a transition from the first yield interaction surface to the fully plastic interaction surface is postulated based on a proposed refined spring stiffness, which formulates the load-displacement relation for material non-linearity under the interaction of bending and axial actions. This produces a benign method for a beam-column composite element under general loading cases. Another main feature of this paper is that, for members containing a point of contraflexure, its location is determined with a simple application of the method herein and a node is then located at this position to reproduce the real flexural behaviour and associated material non-linearity of the member. Recourse is made to an updated Lagrangian formulation to consider geometric non-linear behaviour and to develop a non-linear solution strategy. The formulation with the refined plastic hinge approach is efficacious and robust, and so a full frame analysis incorporating geometric and material non-linearity is tractable. By way of contrast, the plastic zone approach possesses the drawback of strain-based procedures which rely on determining plastic zones within a cross-section and which require lengthwise integration. Following development of the theory, its application is illustrated with a number of varied examples.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

Local inter-session variability modelling for object classification

Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset.

Tools for successful NRM in the Lake Eyre Basin : achieving effective engagement

This chapter was developed as part of the ‘People, communities and economies of the Lake Eyre Basin’ project. It has been written for communities, government agencies and interface organisations involved in natural resource management (NRM) in the Lake Eyre Basin (LEB). Its purpose is to identify the key factors for successful community engagement processes relevant to the LEB and present tools and principles for successful engagement processes. The term ‘interface organisation’ is used here to refer to the diverse range of local and regional organisations (such as Catchment Committees or NRM Regional Bodies) that serve as linkages, or translators, between local communities and broader Australian and State Governments. The importance of fostering and harnessing effective processes of community engagement has been identified as crucial to building a prosperous future for rural and remote regions in Australia. The chapter presents an overview of the literature on successful community engagement processes for NRM, as well as an overview of the current NRM arrangements in the LEB. The main part of the chapter presents findings of the series of interviews conducted with the government liaison officers representing both state and federal organisations who are responsible for coordinating and facilitating regional NRM in the LEB, and with the members of communities of the LEB.

Object tracking via non-Euclidean geometry : a Grassmann approach

A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.

Random projections on manifolds of symmetric positive definite matrices for image classification

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.

Summarisation of short-term and long-term videos using texture and colour

We present a novel approach to video summarisation that makes use of a Bag-of-visual-Textures (BoT) approach. Two systems are proposed, one based solely on the BoT approach and another which exploits both colour information and BoT features. On 50 short-term videos from the Open Video Project we show that our BoT and fusion systems both achieve state-of-the-art performance, obtaining an average F-measure of 0.83 and 0.86 respectively, a relative improvement of 9% and 13% when compared to the previous state-of-the-art. When applied to a new underwater surveillance dataset containing 33 long-term videos, the proposed system reduces the amount of footage by a factor of 27, with only minor degradation in the information content. This order of magnitude reduction in video data represents significant savings in terms of time and potential labour cost when manually reviewing such footage.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Promoting physical activity to older adults in general practice

This study aimed to assess the efficacy of a general practice based intervention to increase physical activity (PA) levels among 50-70 year old adults. One hundred and thirty-six inactive patients (50-70 years) were randomised into three groups. All participants received brief advice and a written prescription from a GP. Group one received this 'usual care' only (GP, n=46); group two received individualised counselling and follow-up contact from an Exercise Scientist (ES, n=45); group three received a pedometer to supplement the ES counselling (PED, n=45). The Active Australia Survey was administered at baseline, after the 12- week intervention and at a 24-week follow-up. One-way ANOVA showed no significant group differences at baseline in self-reported PA. Average time spent walking increased in all three groups at the 24-week follow-up (GP, 68158min/wk, p=0.006; ES, 83160min/wk, p=0.001; PED, 87132min/wk, p<0.001). Total time in PA (weighted min/wk) also increased significantly in all three groups (GP, 98 213min/wk, p=0.003; ES, 108 182min/wk, p<0.001; PED, 158 229min/wk, p<0.001 ). The proportion of participants who initially did not meet National PA Guidelines (150 minutes and 5 sessions/week) but who met the Guidelines at the 12 and 24-week follow-up was 15% (12 weeks) and 20% (24 weeks) in the GP group compared with 36% and 24% in the ES group and 20% and 42% in the PED group. All three intervention strategies were effective in increasing PA, but the ES intervention resulted in a higher proportion of active participants after 12 weeks and the PED group resulted in a higher proportion of active participants after 24 weeks.

Outback institutions : an application of the Institutional Analysis and Development (IAD) framework to four case studies in Australia’s Outback -- Part 4 : Incentives for community participation and land use decisions in Etheridge Shire, Queensland

Natural resource management planning in the Northern Gulf region of Queensland is concerned with ‘how [natural assets] and community aspirations can be protected and enhanced to provide the Northern Gulf community with the economic, social and environmental means to meet the continuing growth of the region in an ecological and economically sustainable way’ (McDonald & Dawson 2004). In the Etheridge Shire, located in the tropical savanna of the Northern Gulf region, two of the activities that influence the balance between economic growth and long-term sustainable development are: 1. the land-use decisions people in the Shire make with regards to their own enterprises. 2. their decisions to engage in civically-minded activities aimed at improving conditions in the region. Land-use decision and engagement in community development activities were chosen for detailed analysis because they are activities for which policies can be devised to improve economic and sustainable development outcomes. Changing the formal and informal rules that guide and govern these two different kinds of decisions that people can make in the Etheridge Shire – the decision to improve one’s own situation and the decision to improve the situation for others in the community – may expand the set of available options for people in the Shire to achieve their goals and aspirations. Identifying appropriate and effective changes in rules requires, first, an understanding of the ‘action arena’, in this case comprised of a diversity of ‘participants’ from both within and outside the Etheridge Shire, and secondly knowledge of ‘action situations’ (land-use decisions and engagement in community development activities) in which stakeholders are involved and/or have a stake. These discussions are presented in sections 4.1.1.1 and 4.1.1.2.

Low-cost railway level crossings - breaking down the barriers

Level crossing risk continues to be a significant safety concern for the security of rail operations around the world. Over the last decade or so, a third of railway related fatalities occurred as a direct result of collisions between road and rail vehicles in Australia. Importantly, nearly half of these collisions occurred at railway level crossings with no active protection, such as flashing lights or boom barriers. Current practice is to upgrade level crossings that have no active protection. However, the total number of level crossings found across Australia exceed 23,500, and targeting the proportion of these that are considered high risk (e.g. public crossings with passive controls) would cost in excess of AU$3.25 billion based on equipment, installation and commissioning costs of warning devices that are currently type approved. Level crossing warning devices that are low-cost provide a potentially effective control for reducing risk; however, over the last decade, there have been significant barriers and legal issues in both Australia and the US that have foreshadowed their adoption. These devices are designed to have significantly lower lifecycle costs compared with traditional warning devices. They often make use of use of alternative technologies for train detection, wireless connectivity and solar energy supply. This paper describes the barriers that have been encountered for the adoption of these devices in Australia, including the challenges associated with: (1) determining requisite safety levels for such devices; (2) legal issues relating to duty of care obligations of railway operators; and (3) issues of Tort liability around the use of less than fail-safe equipment. This paper provides an overview of a comprehensive safety justification that was developed as part of a project funded by a collaborative rail research initiative established by the Australian government, and describes the conceptual framework and processes being used to justify its adoption. The paper provides a summary of key points from peer review and discusses prospective barriers that may need to be overcome for future adoption. A successful outcome from this process would result in the development of a guideline for decision-making, providing a precedence for adopting low-cost level crossing warning devices in other parts of the world. The framework described in this paper also provides relevance to the review and adoption of analogous technologies in rail and other safety critical industries.

Understanding and analyzing a large collection of archived swimming videos

In elite sports, nearly all performances are captured on video. Despite the massive amounts of video that has been captured in this domain over the last 10-15 years, most of it remains in an 'unstructured' or 'raw' form, meaning it can only be viewed or manually annotated/tagged with higher-level event labels which is time consuming and subjective. As such, depending on the detail or depth of annotation, the value of the collected repositories of archived data is minimal as it does not lend itself to large-scale analysis and retrieval. One such example is swimming, where each race of a swimmer is captured on a camcorder and in-addition to the split-times (i.e., the time it takes for each lap), stroke rate and stroke-lengths are manually annotated. In this paper, we propose a vision-based system which effectively 'digitizes' a large collection of archived swimming races by estimating the location of the swimmer in each frame, as well as detecting the stroke rate. As the videos are captured from moving hand-held cameras which are located at different positions and angles, we show our hierarchical-based approach to tracking the swimmer and their different parts is robust to these issues and allows us to accurately estimate the swimmer location and stroke rates.

Predicting movie ratings from audience behaviors

We propose a method of representing audience behavior through facial and body motions from a single video stream, and use these features to predict the rating for feature-length movies. This is a very challenging problem as: i) the movie viewing environment is dark and contains views of people at different scales and viewpoints; ii) the duration of feature-length movies is long (80-120 mins) so tracking people uninterrupted for this length of time is still an unsolved problem, and; iii) expressions and motions of audience members are subtle, short and sparse making labeling of activities unreliable. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input. We then utilize motion-history features which capture the subtle movements of a person within a pre-defined volume, and then form a group representation of the audience by a histogram of pair-wise correlations over a small-window of time. Using this group representation, we learn our movie rating classifier from crowd-sourced ratings collected by rottentomatoes.com and show our prediction capability on audiences from 30 movies across 250 subjects (> 50 hrs).