Visual guidance during competition performance and run-through training in long jumping

To ensure precise foot placement on the take-off board, long jumpers visually regulate their stride pattern during their run-up. A relationship between how much visual guidance they use and the horizontal distance they jump has not, however, been quantified. Run-up precision is often practiced using run-throughs, which exclude the take-off and, therefore, the high physical stress of the complete long jump. The validity with which this common training method simulates the long jump approach remains, however, to be verified. Four state-standard long jumpers and two heptathletes completed two sessions, each comprising six run-throughs and six competition long jumps. A 50 Hz video camera was manually panned from an elevated platform to film each trial, to enable subsequent gait characteristic evaluations. Linear regression analyses identified that a longer visual regulation phase, measured in time, distance or number of strides, was a key predictor of long jump distance. The number of strides that were visually regulated during the long jump approach was, accordingly, positively correlated with long jump distance (r = 0.67, p = 0.001). The amount of visual regulation used during run-throughs was, however, less than half (p = 0.001) of that observed during long jump approaches. Our results should compel long jump coaches to supplement run-through training with additional visual guidance exercises, to encourage their athletes to visually regulate more of their long jump approach.

Examination of the validity and usefulness of the video suggestibility scale for children

The Video Suggestibility Scale for Children (VSSC) was developed by Scullin and colleagues (Scullin & Ceci, 2000; Scullin & Hembrooke, 1998) as a tool for discriminating between children who have different levels of suggestibility. The scale requires children to view a 5-minute video about a birthday party, and to subsequently participate in an interview consisting of 18 yes/no questions. The VSSC consists of two main subscales; Yield (a measure of children's willingness to respond affirmatively to misleading questions about the video) and Shift (a measure of the children's tendency to change their responses after negative feedback from the interviewer). Preliminary research by Scullin and colleagues suggested that the scale possesses satisfactory internal consistency and that children's scores on the VSSC can predict their performance in another suggestibility paradigm. This thesis presents two studies, which further examine the validity and usefulness of the VSSC in an Australian sample of 3- to-5-year-old children. In Study One, children's performance on the VSSC (N = 77) was compared to their performance using other measures of suggestibility. These measures included children's willingness to assent to a false event as well as the number of false interviewer suggestions and new false details that the children provided in their accounts about an independent true-biased and an independent false (non-experienced) event. An independent samples t-test revealed that those children who assented to the false activity generated higher scores on the Yield measure. This pattern was also observed for the Shift subscale although it was not significant. Hierarchical regression analyses revealed that Yield was a significant predictor of the number of false details reported about the false activity, but not the true-biased activity. There was no significant relationship between the Shift Vlll subscale and any of the dependent variables. Overall this study provided partial support for the construct validity of the VSSC. However, it indicated that children's performance on this scale may not be generalisable across different contexts and interview paradigms, and that the Yield subscale is more generalisable than the Shift subscale. Study Two examined whether various group and individual factors that have previously been shown to relate suggestibility (i.e., age, IQ, memory, socio-economic status, gender, temperament) could predict suggestibility as measured by the VSSC. Two hundred and twenty children were recruited from kindergartens, and were divided into two broad socio-economic categories (based predominantly on income). Hierarchical regression analyses revealed that age, intelligence and memory inversely predicted children's Yield suggestibility. Further, children of low socio-economic backgrounds were more suggestible than children of high socio-economic background, and boys were more suggestible than girls on the Yield measure. Although shyness and other internalised and externalised characteristics were explored, no reliable significant relationships were found with Yield. With regard to the Shift subscale, no reliable relationships were found for any of the independent variables except for SES. Overall, results of Study 2 indicated that the VSSC is a potentially useful measure for discriminating between children's suggestibility on the basis on their individual characteristics, although benefits were observed mainly in relation to the Yield subscale. With reference to the findings of these two studies, the potential contribution of the VSSC for research and applied forensic contexts was discussed.

Performance analysis of cyber security awareness delivery methods

In order to decrease information security threats caused by human-related vulnerabilities, an increased concentration on information security awareness and training is necessary. There are numerous information security awareness training delivery methods. The purpose of this study was to determine what delivery method is most successful in providing security awareness training. We conducted security awareness training using various delivery methods such as text based, game based and a short video presentation with the aim of determining user preference delivery methods. Our study suggests that a combined delvery methods are better than individual secrity awareness delivery method.

Improved fade and dissolve detection for reliable video segmentation

We present improved algorithms for automatic fade and dissolve detection in digital video analysis. We devise new two-step algorithms for fade and dissolve detection and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from early work in scene change detection which focuses on identifying the existence of a transition rather than its precise temporal extent. We evaluate our algorithms against two other commonly used methods on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

New enhancements to cut, fade, and dissolve detection processes in video segmentation

We present improved algorithms for cut, fade, and dissolve detection which are fundamental steps in digital video analysis. In particular, we propose a new adaptive threshold determination method that is shown to reduce artifacts created by noise and motion in scene cut detection. We also describe new two-step algorithms for fade and dissolve detection, and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from other early work in scene change detection which tends to focus primarily on identifying the existence of a transition rather than its precise temporal extent. We evaluate our improved algorithms against two other commonly used shot detection techniques on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

A unified 2D-3D video scene change detection framework for mobile camera platforms

In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approach integrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms the scene changes by finding inconsistent appearances in a set of aligned images using the classical MRF background subtraction technique. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from a camera placed on a moving vehicle and the experiments show that our proposed method outperforms previous works in scene change detection, which suggested the feasibility of our approach.

Passenger monitoring in moving bus video

In this paper, we present a novel person detection system for public transport buses tackling the problem of changing illumination conditions. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modeling mechanism with a human shape model into a weighted Bayesian framework to detect passengers on-board buses. SIFT background modeling extracts local stable features on the pre-annotated background seat areas and tracks these features over time to build a global statistical background model for each seat. Since SIFT features are partially invariant to lighting, this background model can be used robustly to detect the seat occupancy status even under severe lighting changes. The human shape model further confirms the existence of a passenger when a seat is occupied. This constructs a robust passenger monitoring system which is resilient to illumination changes. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from bus cameras and the experimental results show that it is superior to state-of-art people detection systems.

Segmentation of intentional human gestures for sports video annotation

We present results on the recognition of intentional human gestures for video annotation and retrieval. We define a gesture as a particular, repeatable, human movement having a predefined meaning. An obvious application of the work is in sports video annotation where umpire gestures indicate specific events. Our approach is to augment video with data obtained from accelerometers worn as wrist bands by one or more officials. We present the recognition performance using a Hidden Markov Model approach for gesture modeling with both isolated gestures and gestures segmented from a stream.

Video genre categorization using audio wavelet coefficients

In this paper, we investigate the use of a wavelet transform-based analysis of audio tracks accompanying videos for the problem of automatic program genre detection. We compare the classification performance based on wavelet-based audio features to that using conventional features derived from Fourier and time analysis for the task of discriminating TV programs such as news, commercials, music shows, concerts, motor racing games, and animated cartoons. Three different classifiers namely the Decision Trees, SVMs, and k-Nearest Neighbours are studied to analyse the reliability of the performance of our wavelet features based approach. Further, we investigate the issue of an appropriate duration of an audio clip to be analyzed for this automatic genre determination. Our experimental results show that features derived from the wavelet transform of the audio signal can very well separate the six video genres studied. It is also found that there is no significant difference in performance with varying audio clip durations across the classifiers.

Multi-modal abnormality detection in video with unknown data segmentation

This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras; and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days.

Undergraduate nursing students' performance in recognising and responding to sudden patient deterioration in high psychological fidelity simulated environments: an Australian multi-centre study

Objectives
This paper reports the quantitative findings of the first phase of a larger program of ongoing research: Feedback Incorporating Review and Simulation Techniques to Act on Clinical Trends (FIRST2ACTTM). It specifically aims to identify the characteristics that may predict primary outcome measures of clinical performance, teamwork and situation awareness in the management of deteriorating patients.

Design
Mixed-method multi-centre study.

Setting
High fidelity simulated acute clinical environment in three Australian universities.

Participants
A convenience sample of 97 final year nursing students enrolled in an undergraduate Bachelor of Nursing or combined Bachelor of Nursing degree were included in the study.

Method
In groups of three, participants proceeded through three phases: (i) pre-briefing and completion of a multi-choice question test, (ii) three video-recorded simulated clinical scenarios where actors substituted real patients with deteriorating conditions, and (iii) post-scenario debriefing. Clinical performance, teamwork and situation awareness were evaluated, using a validated standard checklist (OSCE), Team Emergency Assessment Measure (TEAM) score sheet and Situation Awareness Global Assessment Technique (SAGAT). A Modified Angoff technique was used to establish cut points for clinical performance.

Results
Student teams engaged in 97 simulation experiences across the three scenarios and achieved a level of clinical performance consistent with the experts' identified pass level point in only 9 (1%) of the simulation experiences. Knowledge was significantly associated with overall teamwork (p = .034), overall situation awareness (p = .05) and clinical performance in two of the three scenarios (p = .032 cardiac and p = .006 shock). Situation awareness scores of scenario team leaders were low overall, with an average total score of 41%.

Conclusions
Final year undergraduate nursing students may have difficulty recognising and responding appropriately to patient deterioration. Improving pre-requisite knowledge, rehearsal of first response and team management strategies need to be a key component of undergraduate nursing students' education and ought to specifically address clinical performance, teamwork and situation awareness.

A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.

The elimination race in track cycling : patterns and predictors of performance

The track cycling Omnium is a multi-event competition that has recently been expanded to include the Elimination Race (ER), which presents a unique set of physical and tactical demands. The purpose of this research was to characterise the performance attributes of successful and unsuccessful cyclists in the ER, that are also predictive of performance. Video recordings of four international level ERs were analysed. The performance attributes measured related to the cyclists’ velocity and two dimensional position in the peloton. The average velocity of the peloton up to lap 30 (of 50) was relatively high and consistent (52.2±1.5 km/h). After lap 30, there was a significant (p<0.001) change in velocity (49.9±2.4 km/h), characterised by more fluctuations in lap-to-lap velocity. Successful ER cyclists adopted a tactic of remaining in the middle of the peloton, in the lower lanes of the velodrome, thus avoiding the risk of elimination at the rear and the extra effort required to remain on the front of the peloton. Unsuccessful cyclists tended to reside in the rear and upper (higher) portions of the peloton, risking elimination more often and having to ride faster than those in the lower lanes of the velodrome. The physiological demands of the Elimination Race that are determined by velocity, vary throughout the Elimination Race and the pattern of movement within the peloton is different for successful and unsuccessful cyclists. The findings of the present study may confirm some aspects of race tactics that are currently thought to be optimal, but they also reveal novel information that is useful to coaches and cyclists who compete in the Elimination Race.

The man who mistook Marat for Sade: 'living' memory and the video archive

Digital video archives, which are growing at an exponential rate, will become increasingly important to Theatre History and Performance Studies, and questions of how scholars negotiate the relationships between memory, technology and performance events in theoretical and practical terms will become crucial. Indeed, there is already a considerable body of scholarly material on this topic. This article considers these questions with specific reference to the relationship between video records deposited in digital archives and human memory. First and foremost, this article raises questions about the authority of the archive and the ways in which archival technologies, in the words of Maaike Bleeker, 'transform how we remember, how our and others' memories are entangled in the here-and-now, and, in the end, even how we think and imagine'.