3D thermal mapping of building interiors using an RGB-D and thermal camera

The building sector is the dominant consumer of energy and therefore a major contributor to anthropomorphic climate change. The rapid generation of photorealistic, 3D environment models with incorporated surface temperature data has the potential to improve thermographic monitoring of building energy efficiency. In pursuit of this goal, we propose a system which combines a range sensor with a thermal-infrared camera. Our proposed system can generate dense 3D models of environments with both appearance and temperature information, and is the first such system to be developed using a low-cost RGB-D camera. The proposed pipeline processes depth maps successively, forming an ongoing pose estimate of the depth camera and optimizing a voxel occupancy map. Voxels are assigned 4 channels representing estimates of their true RGB and thermal-infrared intensity values. Poses corresponding to each RGB and thermal-infrared image are estimated through a combination of timestamp-based interpolation and a pre-determined knowledge of the extrinsic calibration of the system. Raycasting is then used to color the voxels to represent both visual appearance using RGB, and an estimate of the surface temperature. The output of the system is a dense 3D model which can simultaneously represent both RGB and thermal-infrared data using one of two alternative representation schemes. Experimental results demonstrate that the system is capable of accurately mapping difficult environments, even in complete darkness.

AR Services Video

Video presented as part of the USECA 2011 workshop at WISE 2011. Real-time sales assistant service is a problematic component of remote delivery of sales support for customers. Solutions involving web pages, telephony and video support prove problematic when seeking to remotely guide customers in their sales processes, especially with transactions revolving around physically complex artefacts. This process involves a number of services that are often complex in nature, ranging from physical compatibility and configuration factors, to availability and credit services. We propose the application of a combination of virtual worlds and augmented reality to create synthetic environments suitable for remote sales of physical artefacts, right in the home of the purchaser. A high level description of the service structure involved is shown, along with a use case involving the sale of electronic goods and services within an example augmented reality application. We expect this work to have application in many sales domains involving physical objects needing to be sold over the Internet.

Automatic object segmentation of unstructured scenes using colour and depth maps

This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results.

Caught on camera

The emergence of shopping malls in Europe, the UK and Australia over the last thirty years or so, raises questions about the disruptive effects of such capital intensive developments on local area shopping facilities, transport and other infrastructures and the maintenance of artificially high prices for goods, whereby the promised greater choice of shops and prices is rarely a genuine free market of competition leading to lower prices. A central question to be addressed is whom these centres represent and belong to. While many claim to exist to ‘serve the community’ almost all malls and centres are private property and the community of shoppers has few, if any rights compared with the conventional high street, which is a public thoroughfare. This permits the management of the centres through their own private security staff, to observe, follow, eject and refuse further admission to anyone considered to be ‘undesirable’. What is different about the newest shopping centres is the routine use of increasingly sophisticated CCTV surveillance equipment to observe and record, for later evidential and entry restriction use, the movements of centre visitors.

Camera trapping and invasions of privacy : an Australian legal perspective

Camera trapping is a scientific survey technique that involves the placement of heat-and motion-sensing automatic triggered cameras into the ecosystem to record images of animals for the purpose of studying wildlife. As technology continues to advance in sophistication, the use of camera trapping is becoming more widespread and is a crucial tool in the study of, and attempts to preserve, various species of animals, particularly those that are internationally endangered. However, whatever their value as an ecological device, camera traps also create a new risk of incidentally and accidentally capturing images of humans who venture into the area under surveillance. This article examines the current legal position in Australia in relation to such unintended invasions of privacy. It considers the current patchwork of statute and common laws that may provide a remedy in such circumstances. It also discusses the position that may prevail should the recommendations of either the Australian Law Reform Commission and/or New South Wales Law Reform Commission be adopted and a statutory cause of action protecting personal privacy be enacted.

Improving level crossing safety through enhanced data recording and reporting : The CRC for Rail Innovation’s Baseline Rail Level Crossing Video project

This paper describes the work being conducted in the baseline rail level crossing project, supported by the Australian rail industry and the Cooperative Research Centre for Rail Innovation. The paper discusses the limitations of near-miss data for analysis obtained using current level crossing occurrence reporting practices. The project is addressing these limitations through the development of a data collection and analysis system with an underlying level crossing accident causation model. An overview of the methodology and improved data recording process are described. The paper concludes with a brief discussion of benefits this project is expected to provide the Australian rail industry.

Transitioning from dependent to more independent learners : Using video tutorials as a means of helping students help themselves

This paper examines the use of short video tutorials in a post-graduate accounting subject, as a means of helping students transition from dependent to more independent learners. Five short (three to five minute) video tutorials were introduced in an effort to shift the reliance for learning from the lecturer to the student. Students’ usage of video tutorials, comments by students, and reliance on teaching staff for individual assistance were monitored over three semesters from 2008 to 2009. Interviews with students were then conducted in late 2009 to more comprehensively evaluate the use and benefits of video tutorials. Findings reveal preliminary but positive outcomes in terms of both more efficient teaching and more effective learning.

Scene invariant multi camera crowd counting

Automated crowd counting has become an active field of computer vision research in recent years. Existing approaches are scene-specific, as they are designed to operate in the single camera viewpoint that was used to train the system. Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap. This paper proposes a novel scene invariant crowd counting algorithm that is designed to operate across multiple cameras. The approach uses camera calibration to normalise features between viewpoints and to compensate for regions of overlap. This compensation is performed by constructing an 'overlap map' which provides a measure of how much an object at one location is visible within other viewpoints. An investigation into the suitability of various feature types and regression models for scene invariant crowd counting is also conducted. The features investigated include object size, shape, edges and keypoints. The regression models evaluated include neural networks, K-nearest neighbours, linear and Gaussian process regresion. Our experiments demonstrate that accurate crowd counting was achieved across seven benchmark datasets, with optimal performance observed when all features were used and when Gaussian process regression was used. The combination of scene invariance and multi camera crowd counting is evaluated by training the system on footage obtained from the QUT camera network and testing it on three cameras from the PETS 2009 database. Highly accurate crowd counting was observed with a mean relative error of less than 10%. Our approach enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system.

Sensing place : embodiment, sensoriality, kinesis, and children behind the camera

This article is a call to literacy teachers and researchers to embrace the possibility of attending more consciously to the senses in digital media production. Literacy practices do not occur only in the mind, but involve the sensoriality, embodiment, co-presence, and movement of bodies. This paper theorises the sensorial and embodied dimension of children’s filmmaking about place in two communities in Australia. The films were created by pre-teen Indigenous and non-Indigenous children in Logan, Queensland, and by Indigenous teenagers at the Warralong campus of the Strelley Community School in remote Western Australia. The films were created through engagement in cross-curricular units that sensitised the students’ experience of local places, gathering corporeal information through their sensing bodies as they interacted with the local ecology. The analysis highlights how the sensorial and bodily nature of literacy practice through documentary filmmaking was central to the children’s formation and representation of knowledge, because knowledge and literacy practices are not only acquired through the mind, but are also reliant on embodiment, sensoriality, co-presence, and kinesics of the body in place.

User-centred study on quality of mobile video services

Mobile video, as an emerging market and a promising research field, has attracted much attention from both industry and researchers. Considering the quality of user-experience as the crux of mobile video services, this chapter aims to provide a guide to user-centered studies of mobile video quality. This will benefit future research in better understanding user needs and experiences, designing effective research, and providing solid solutions to improve the quality of mobile video. This chapter is organized in three main parts: (1) a review of recent user studies from the perspectives of research focuses, user study methods, and data analysis methods; (2) an example of conducting a user study of mobile video research, together with the discussion on a series of relative issues, such as participants, materials and devices, study procedure, and analysis results, and; (3) a conclusion with an open discussion about challenges and opportunities in mobile video related research, and associated potential future improvements.

Visualising experimental flow fields through a stormwater gross pollutant trap

Abstract An experimental dataset representing a typical flow field in a stormwater gross pollutant trap (GPT) was visualised. A technique was developed to apply the image-based flow visualisation (IBFV) algorithm to the raw dataset. Particle image velocimetry (PIV) software was previously used to capture the flow field data by tracking neutrally buoyant particles with a high speed camera. The dataset consisted of scattered 2D point velocity vectors and the IBFV visualisation facilitates flow feature characterisation within the GPT. The flow features played a pivotal role in understanding stormwater pollutant capture and retention behaviour within the GPT. It was found that the IBFV animations revealed otherwise unnoticed flow features and experimental artefacts. For example, a circular tracer marker in the IBFV program visually highlighted streamlines to investigate the possible flow paths of pollutants entering the GPT. The investigated flow paths were compared with the behaviour of pollutants monitored during experiments.

Perspectives on schooling from early adolescent video diaries

The aim of this project was to gain the voice of the early adolescent (aged between 11 and 13 years) about the things that are genuinely important to them in their lives. Eight participants were asked to record a private video diary entry each night for one week. A number of thematic topics were identified including: their experiences and perspectives on school curriculum and assessment, opinions about schooling structures, and importance of friendship and family. Giving young adolescents the opportunity to voice their opinions has been valuable in gaining insight to the relative impacts of teaching and learning approaches in their school contexts and the issues they consider as the most important in their lives.

A framework of Dynamic Difficulty Adjustment in competitive multiplayer video games

The balance between player competence and the challenge presented by a task has been acknowledged as a major factor in providing optimal experience in video games. While Dynamic Difficulty Adjustment (DDA) presents methods for adjusting difficulty in real-time during singleplayer games, little research has explored its application in competitive multiplayer games where challenge is dictated by the competence of human opponents. By conducting a formal review of 180 existing competitive multiplayer games, it was found that a large number of modern games are utilizing DDA techniques to balance challenge between human opponents. From this data, we propose a preliminary framework for classifying Multiplayer Dynamic Difficulty Adjustment (mDDA) instances.

West End : seat of power? (Griffith, QLD)

This video details interviews undertaken and shows a range of varying local opinions in West End within the Griffith electorate of Brisbane in the lead up to 2007's federal election. In 2007, West Enders had both the sitting Queensland Premier, Anna Bligh, and the impending Prime Minister, Kevin Rudd, as their local members. This video was produced as part of the YouDecide2007, a non-profit citizen journalism intiative led by QUT's Creative Industries and funded by the ARC. The camera man and editor was Barry Saunders.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.