Origins and current issues in Quiet Eye research

All sports require precise control of physical actions and vision is essential in providing the information the movement systems needs to perform at a high level. Vision and focus of attention play a critically important role as the ability to direct the gaze to optimal areas in the playing environment, at the appropriate time, is central to success in all sports. One variable that has been consistently found to discriminate elite performers from their near-elite and novice counterparts is the Quiet Eye (QE). In the present paper, I first define the QE, followed by an explanation of its origins as well as the question: why have I pursued this one variable for over 35 years? I then provide a brief overview of QE research, and concentrate on QE training, which has emerged as an effective method for improving both attentional focus and motor performance. In the final section, I discuss some future directions, in particular those related to identifying the neural networks underlying the QE during successful trials

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

New data analytics and visualization methods in personal data mining, cancer data analysis and sports data visualization

In this dissertation, we discuss a reading profiling system, a biological data visualization system and a sports visualization system. Self-tracking is getting increasingly popular in the field of personal informatics. Reading profiling can be used as a personal data collection method. We present UUAT, an unintrusive user attention tracking system. In UUAT, we used user interaction data to develop technologies that help to pinpoint a users reading region (RR). Based on computed RR and user interaction data, UUAT can identify a readers reading struggle or interest. A biomarker is a measurable substance that may be used as an indicator of a particular disease. We developed CancerVis for visual and interactive analysis of cancer data and demonstrate how to apply this platform in cancer biomarker research. CancerVis provides interactive multiple views from different perspectives of a dataset. The views are synchronized so that users can easily link them to a same data entry. Furthermore, CancerVis supports data mining practice in cancer biomarker, such as visualization of optimal cutpoints and cutthrough exploration. Tennis match summarization helps after-live sports consumers assimilate an interested match. We developed TennisVis, a comprehensive match summarization and visualization platform. TennisVis offers chart- graph for a client to quickly get match facts. Meanwhile, TennisVis offers various queries of tennis points to satisfy diversified client preferences (such as volley shot, many-shot rally) of tennis fans. Furthermore, TennisVis offers video clips for every single tennis point and a recommendation rating is computed for each tennis play. A case study shows that TennisVis identifies more than 75% tennis points in full time match

MotionScan: Towards Brain Concussion Detection with a Mobile Tablet Device

This thesis reports on a study to determine the viability of using a mobile tablet device as a brain concussion detection tool. The research builds upon the results of a prior method of collecting data for measuring motion sensitivity, where a user presses and releases a force sensor to balance a rising and falling line on a computer display. The motion sensitivity data collected using this force sensor device was shown to have less irregularity in persons with concussion. The MotionScan application, developed for this research, uses the accelerometer of a tablet device to record motor movement of a user while the user tries to control a free-moving ball on the tablet screen to trace a line. Data collection sessions were conducted with 20 participants, where researchers recorded motor performance data for similar tasks using both the MotionScan application and the force sensor device. Researchers analyzed the performance outcomes on the tablet application and force sensor device, and validated that they both record motor movements similarly. Participants were also asked for their feedback on the interface of MotionScan and the data collection process, which was used to improve the usability of MotionScan and data collection processes. The research demonstrates that a tablet device can measure the variability in a person’s motor sensitivity and with more research could be used as a concussion detection tool

Gaze behaviour of volleyball players during successful serve reception

The aim of this study was to identify and compare the gaze behaviour of both advancement levels (Advanced and Not Advanced) and genders (female and male) during successful serve reception. A quantitative, exploratory and quasi-experimental research design was used in which 50 ToppVolley Norway student athletes aged 16 to 19 years were sampled. The gaze behaviour of junior volleyball athletes was assesed as they received two types of serves (1 float and 1 top spin jump serve) and performed a forearm and/or overhead pass to a setter’s target while wearing an ASL mobile eye tracker. Gaze characteristics such as fixation number, fixation duration and areas of interest were used to achieve the aim of the study. The study found that Advanced participants differed from the Not Advanced athletes in employing fewer (9.70 ± 1.14 versus 10.77 ± 3.63 , p.20 ) fixations but for longer durations per fixations (1.64 ± 0.20 versus 1.60 ± 0.34, p.20 ) in receiving the float serve. Contrary, for the reception of the top spin jump serve, the Advanced athletes employed more fixations (12.11 ± 2.40 versus 11.83 ± 2.17, p.20 ) but for shorter durations per fixations (1.57 ± 0.26 versus 1.65 ± 0.34 sec, p.20 ) than the Not Advanced athletes. Male athletes in this study were more experienced than their female counterparts (5.97 ± 1.62 years versus 4.75 ± 1.59 years, p.20 ) with males employing more fixations than the females (11.02 ± 3.63 versus 9.19 ± 1.55 and 12.26 ± 2.46 versus 11.36 ± 1.69, p.20 ) for the float and top spin serves respectively but for shorter durations per fixation (1.62 ± 0.27 versus 1.76 ± 0.29 sec (float) and 1.56 ± 0.32 versus 1.73 ± 0.28 sec (top spin) p.20 ) than the female athletes. These results and findings suggests that Advanced athletes for both serves attended to the most appropriate visual information through the top-down approach, their knowledge and past experiences. For gender differences, the results show that the female athletes employed fewer fixation points in receiving float serves, however the employment of fewer fixation points during the top spin jump serves was due to receiving serves characterised by easily identifiable trajectories and lower speeds. The contradicting finding of the Advanced athletes employing more fixation points for the top spin jump serve may be due to task complexity demands. Thirteen areas of interests were also identified. The combined results for both gender and advancement levels suggest that the athletes fixated on similar areas of interests, primarily the upper body and secondary on the ball (flight), serve reception phase, arrival at target and contact point. The aim and objectives of this study were achieved in that both absolute and relative values for number of fixations, duration of fixations and areas of interest fixated on, were established. However the outcome of comparisons made, were not all expected particularly that of the Advanced group for the top spin jump serve. Possible explanations were offered, but further research is required in this regard

A Novel Multi-View Table Tennis Umpiring Framework

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status. While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies. In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development

Domain anomaly detection in machine perception: a system architecture and taxonomy

We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifacetted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature

Training Algorithms for Multiple Object Tracking

Multiple object tracking is a crucial Computer Vision Task. It aims at locating objects of interest in the image sequences, maintaining their identities, and identifying their trajectories over time. A large portion of current research focuses on tracking pedestrians, and other types of objects, that often exhibit predictable behaviours, that allow us, as humans, to track those objects. Nevertheless, most existing approaches rely solely on simple affinity or appearance cues to maintain the identities of the tracked objects, ignoring their behaviour. This presents a challenge when objects of interest are invisible or indistinguishable for a long period of time. In this thesis, we focus on enhancing the quality of multiple object trackers by learning and exploiting the long ranging models of object behaviour. Such behaviours come in different forms, be it a physical model of the ball motion, model of interaction between the ball and the players in sports or motion patterns of pedestrians or cars, that is specific to a particular scene. In the first part of the thesis, we begin with the task of tracking the ball and the players in team sports. We propose a model that tracks both types of objects simultaneously, while respecting the physical laws of ball motion when in free fall, and interaction constraints that appear when players are in the possession of the ball. We show that both the presence of the behaviour models and the simultaneous solution of both tasks aids the performance of tracking, in basketball, volleyball, and soccer. In the second part of the thesis, we focus on motion models of pedestrian and car behaviour that emerge in the outdoor scenes. Such motion models are inherently global, as they determine where people starting from one location tend to end up much later in time. Imposing such global constraints while keeping the tracking problem tractable presents a challenge, which is why many approaches rely on local affinity measures. We formulate a problem of simultaneously tracking the objects and learning their behaviour patterns. We show that our approach, when applied in conjunction with a number of state-of-the-art trackers, improves their performance, by forcing their output to follow the learned motion patterns of the scene. In the last part of the thesis, we study a new emerging class of models for multiple object tracking, that appeared recently due to availability of large scale datasets - sequence models for multiple object tracking. While such models could potentially learn arbitrarily long ranging behaviours, training them presents several challenges. We propose a training scheme and a loss function that allows to significantly improve the quality of training of such models. We demonstrate that simply using our training scheme and loss allows to learn scoring function for trajectories, which enables us to outperform state-of-the-art methods on several tracking benchmarks