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Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels(More)
In this paper, we use ball and player tracking data from “Hawk-Eye” to discover unique player styles and predict within-point events. We move beyond current analysis that only incorporates coarse match statistics (i.e. serves, winners, number of shots, volleys) and use spatial and temporal information which better characterizes the tactics and tendencies of(More)
Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality(More)
After first observing a person, the task of person re-identification involves recognising an individual at different locations across a network of cameras at a later time. Traditionally, this task has been performed by first extracting appearance features of an individual and then matching these features to the previous observation. However, identifying an(More)
In professional sport, an enormous amount of fine-grain performance data can be generated at near millisecond intervals in the form of vision-based tracking data. One of the first sports to embrace this technology has been tennis, where Hawk-Eye technology has been used to both aid umpiring decisions, and to visualize shot trajectories for broadcast(More)
In highly dynamic and adversarial domains such as sports, short-term predictions are made by incorporating both local immediate as well global situational information. For forecasting complex events, higher-order models such as Hidden Conditional Random Field (HCRF) have been used to good effect as capture the long-term, high-level semantics of the signal.(More)
In professional sport, an enormous amount of fine-grain performance data can be generated at near millisecond intervals in the form of vision-based tracking data. One of the first sports to embrace this technology has been tennis, where Hawk-Eye technology has been used to both aid umpiring decisions, and to visualize shot trajectories for broadcast(More)
Tracking objects like a basketball from a monocular view is challenging due to its small size, potential to move at high velocities as well as the high frequency of occlusion. However, humans with a deep knowledge of a game like basketball can predict with high accuracy the location of the ball even without seeing it due to the location and motion of nearby(More)
Automatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a(More)
In soccer, the most frequent event that occurs is a pass. For a trained eye, there are a myriad of adjectives which could describe this event (e.g., "majestic pass", "conservative" to "poor-ball"). However, as these events are needed to be coded live and in real-time (most often by human annotators), the current method of grading passes is restricted to the(More)