Group segmentation during object tracking using optical flow discontinuities

Within a surveillance video, occlusions are commonplace, and accurately resolving these occlusions is key when seeking to accurately track objects. The challenge of accurately segmenting objects is further complicated by the fact that within many real-world surveillance environments, the objects appear very similar. For example, footage of pedestrians in a city environment will consist of many people wearing dark suits. In this paper, we propose a novel technique to segment groups and resolve occlusions using optical flow discontinuities. We demonstrate that the ratio of continuous to discontinuous pixels within a region can be used to locate the overlapping edges, and incorporate this into an object tracking framework. Results on a portion of the ETISEO database show that the proposed algorithm results in improved tracking performance overall, and improved tracking within occlusions.

Molecular analysis of the myosin gene family in Arabidopsis thaliana

Myosin is believed to act as the molecular motor for many actin-based motility processes in eukaryotes. It is becoming apparent that a single species may possess multiple myosin isoforms, and at least seven distinct classes of myosin have been identified from studies of animals, fungi, and protozoans. The complexity of the myosin heavy-chain gene family in higher plants was investigated by isolating and characterizing myosin genomic and cDNA clones from Arabidopsis thaliana. Six myosin-like genes were identified from three polymerase chain reaction (PCR) products (PCR1, PCR11, PCR43) and three cDNA clones (ATM2, MYA2, MYA3). Sequence comparisons of the deduced head domains suggest that these myosins are members of two major classes. Analysis of the overall structure of the ATM2 and MYA2 myosins shows that they are similar to the previously-identified ATM1 and MYA1 myosins, respectively. The MYA3 appears to possess a novel tail domain, with five IQ repeats, a six-member imperfect repeat, and a segment of unique sequence. Northern blot analyses indicate that some of the Arabidopsis myosin genes are preferentially expressed in different plant organs. Combined with previous studies, these results show that the Arabidopsis genome contains at least eight myosin-like genes representing two distinct classes.

Implementing low intensity CBT in case management of clients with severe mental illness

In a previous chapter (Dean and Kavanagh, Chapter 37), the authors made a case for applying low intensity (LI) cognitive behaviour therapy (CBT) to people with serious mental illness (SMI). As in other populations, LI CBT interventions typically deal with circumscribed problems or behaviours. LI CBT retains an emphasis on self-management, has restricted content and segment length, and does not necessarily require extensive CBT training. In applying these interventions to SMI, adjustments may be needed to address cognitive and symptomatic difficulties often faced by these groups. What may take a single session in a less affected population may require several sessions or a thematic application of the strategy within case management. In some cases, the LI CBT may begin to appear more like a high-intensity (HI) intervention, albeit simple and with many LI CBT characteristics still retained. So, if goal setting were introduced in one or two sessions, it could clearly be seen as an LI intervention. When applied to several different situations and across many sessions, it may be indistinguishable from a simple HI treatment, even if it retains the same format and is effectively applied by a practitioner with limited CBT training. ----- ----- In some ways, LI CBT should be well suited to case management of patients with SMI. treating staff typically have heavy workloads, and find it difficult to apply time-consuming treatments (Singh et al. 2003). LI CBT may allow provision of support to greater numbers of service users, and allow staff to spend more time on those who need intensive and sustained support. However, the introduction of any change in practice has to address significant challenges, and LI CBT is no exception. ----- ----- Many of the issues that we face in applying LI CBT to routine case management in a mnetal health service and their potential solutions are essentially the same as in a range of other problem domains (Turner and Sanders 2006)- and, indeed, are similar to those in any adoption of innovation (Rogers 2003). Over the last 20 years, several commentators have described barriers to implementing evidence-based innovations in mental health services (Corrigan et al. 1992; Deane et al. 2006; Kavanagh et al. 1993). The aim of the current chapter is to present a cognitive behavioural conceptualisation of problems and potential solutions for dissemination of LI CBT.

Adapting low intensity CBT interventions for clients with severe mental illness

Many people with severe mental illness (SMI) such as schizophrenia, whose psychotic symptoms are effectively managed, continue to experience significant functional problems. This chapter argues that low intensity (LI) cognitive behaviour therapy (CBT; e.g. for depression, anxiety, or other issues) is applicable to these clients, and that LI CBT can be consistent with long-term case management. However, adjustments to LI CBT strategies are often necessary and boundaries between LI CBT and high intensity (HI) CBT (with more extensive practitioner contact and complexity) may become blurred. Our focus is on LI CBT's self-management emphasis, its restricted content and segment length, and potential use after limited training. In addition to exploring these issues, it draws on the authors' Collaborative Recovery (CR; Oades et al. 2005) and 'Start Over and Survive' programs (Kavanagh et al. 2004) as examples. ----- ----- Evidence for the effectiveness of LI CBT with severe mental illness is often embedded within multicomponent programs. For example, goal setting and therapeutic homework are common components of such programs, but they can also be used as discrete LI CBT interventions. A review of 40 randomised controlled trials involving recipients with schizophrenia or other sever mental illnesses has identified key components of illness management programs (Mueser et al. 2002). However, it is relatively rare for specific components of these complex interventions to be assessed in isolation. Given these constraints, the evidence for specific LI CBT interventions with severe mental ilnness is relatively limited.

The role of vision in obese and normal-weight children's gait control

Previous research has suggested that perceptual-motor difficulties may account for obese children's lower motor competence; however, specific evidence is currently lacking. Therefore, this study examined the effect of altered visual conditions on spatiotemporal and kinematic gait parameters in obese versus normal-weight children. Thirty-two obese and normal-weight children (11.2 ± 1.5 years) walked barefoot on an instrumented walkway at constant self-selected speed during LIGHT and DARK conditions. Three-dimensional motion analysis was performed to calculate spatiotemporal parameters, as well as sagittal trunk segment and lower extremity joint angles at heel-strike and toe-off. Self-selected speed did not significantly differ between groups. In the DARK condition, all participants walked at a significantly slower speed, decreased stride length, and increased stride width. Without normal vision, obese children had a more pronounced increase in relative double support time compared to the normal-weight group, resulting in a significantly greater percentage of the gait cycle spent in stance. Walking in the DARK, both groups showed greater forward tilt of the trunk and restricted hip movement. All participants had increased knee flexion at heel-strike, as well as decreased knee extension and ankle plantarflexion at toe-off in the DARK condition. The removal of normal vision affected obese children's temporal gait pattern to a larger extent than that of normal-weight peers. Results suggest an increased dependency on vision in obese children to control locomotion. Next to the mechanical problem of moving excess mass, a different coupling between perception and action appears to be governing obese children's motor coordination and control.

An assessment system for evaluation of driving competencies

Automobiles have deeply impacted the way in which we travel but they have also contributed to many deaths and injury due to crashes. A number of reasons for these crashes have been pointed out by researchers. Inexperience has been identified as a contributing factor to road crashes. Driver’s driving abilities also play a vital role in judging the road environment and reacting in-time to avoid any possible collision. Therefore driver’s perceptual and motor skills remain the key factors impacting on road safety. Our failure to understand what is really important for learners, in terms of competent driving, is one of the many challenges for building better training programs. Driver training is one of the interventions aimed at decreasing the number of crashes that involve young drivers. Currently, there is a need to develop comprehensive driver evaluation system that benefits from the advances in Driver Assistance Systems. A multidisciplinary approach is necessary to explain how driving abilities evolves with on-road driving experience. To our knowledge, driver assistance systems have never been comprehensively used in a driver training context to assess the safety aspect of driving. The aim and novelty of this thesis is to develop and evaluate an Intelligent Driver Training System (IDTS) as an automated assessment tool that will help drivers and their trainers to comprehensively view complex driving manoeuvres and potentially provide effective feedback by post processing the data recorded during driving. This system is designed to help driver trainers to accurately evaluate driver performance and has the potential to provide valuable feedback to the drivers. Since driving is dependent on fuzzy inputs from the driver (i.e. approximate distance calculation from the other vehicles, approximate assumption of the other vehicle speed), it is necessary that the evaluation system is based on criteria and rules that handles uncertain and fuzzy characteristics of the driving tasks. Therefore, the proposed IDTS utilizes fuzzy set theory for the assessment of driver performance. The proposed research program focuses on integrating the multi-sensory information acquired from the vehicle, driver and environment to assess driving competencies. After information acquisition, the current research focuses on automated segmentation of the selected manoeuvres from the driving scenario. This leads to the creation of a model that determines a “competency” criterion through the driving performance protocol used by driver trainers (i.e. expert knowledge) to assess drivers. This is achieved by comprehensively evaluating and assessing the data stream acquired from multiple in-vehicle sensors using fuzzy rules and classifying the driving manoeuvres (i.e. overtake, lane change, T-crossing and turn) between low and high competency. The fuzzy rules use parameters such as following distance, gaze depth and scan area, distance with respect to lanes and excessive acceleration or braking during the manoeuvres to assess competency. These rules that identify driving competency were initially designed with the help of expert’s knowledge (i.e. driver trainers). In-order to fine tune these rules and the parameters that define these rules, a driving experiment was conducted to identify the empirical differences between novice and experienced drivers. The results from the driving experiment indicated that significant differences existed between novice and experienced driver, in terms of their gaze pattern and duration, speed, stop time at the T-crossing, lane keeping and the time spent in lanes while performing the selected manoeuvres. These differences were used to refine the fuzzy membership functions and rules that govern the assessments of the driving tasks. Next, this research focused on providing an integrated visual assessment interface to both driver trainers and their trainees. By providing a rich set of interactive graphical interfaces, displaying information about the driving tasks, Intelligent Driver Training System (IDTS) visualisation module has the potential to give empirical feedback to its users. Lastly, the validation of the IDTS system’s assessment was conducted by comparing IDTS objective assessments, for the driving experiment, with the subjective assessments of the driver trainers for particular manoeuvres. Results show that not only IDTS was able to match the subjective assessments made by driver trainers during the driving experiment but also identified some additional driving manoeuvres performed in low competency that were not identified by the driver trainers due to increased mental workload of trainers when assessing multiple variables that constitute driving. The validation of IDTS emphasized the need for an automated assessment tool that can segment the manoeuvres from the driving scenario, further investigate the variables within that manoeuvre to determine the manoeuvre’s competency and provide integrated visualisation regarding the manoeuvre to its users (i.e. trainers and trainees). Through analysis and validation it was shown that IDTS is a useful assistance tool for driver trainers to empirically assess and potentially provide feedback regarding the manoeuvres undertaken by the drivers.

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.

Strategies for appropriate technology and competitive performance in infrastructure construction

Earlier research developed theoretically-based aggregate metrics for technology strategy and used them to analyze California bridge construction firms (Hampson, 1993). Determinants of firm performance, including trend in contract awards, market share and contract awards per employee, were used as indicators for competitive performance. The results of this research were a series of refined theoretically-based measures for technology strategy and a demonstrated positive relationship between technology strategy and competitive performance within the bridge construction sector. This research showed that three technology strategy dimensions—competitive positioning, depth of technology strategy, and organizational fit— show very strong correlation with the competitive performance indicators of absolute growth in contract awards, and contract awards per employee. Both researchers and industry professionals need improved understanding of how technology affects results, and how to better target investments to improve competitive performance in particular industry sectors. This paper builds on the previous research findings by evaluating the strategic fit of firms' approach to technology with industry segment characteristics. It begins with a brief overview of the background regarding technology strategy. The major sections of the paper describe niches and firms in an example infrastructure construction market, analyze appropriate technology strategies, and describe managerial actions to implement these strategies and support the business objectives of the firm.