TennisSense: A platform for extracting semantic information from multi-camera tennis data
Ciarán Ó Conaire,Philip Kelly,Damien Connaghan,Noel E. O'Connor +3 more
- pp 1062-1067
TLDR
TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches, is introduced and the algorithms for extracting useful metadata from the overhead court camera are described and evaluated.Abstract:
In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface.read more
Citations
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TenniVis: Visualization for Tennis Match Analysis
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ShuttleSpace: Exploring and Analyzing Movement Trajectory in Immersive Visualization
TL;DR: ShuttleSpace is presented, an immersive analytics system to assist experts in analyzing trajectory data in badminton that leverages the peripheral vision to combine the 2D and 3D visualizations and the VR controller to support natural interactions via a stroke metaphor.
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Ball tracking in sports: a survey
TL;DR: An exhaustive survey of all the published research works on ball tracking in a categorical manner is presented to present discussions on the published work so far and views and opinions followed by a modified block diagram of the tracking process.
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A deep learning ball tracking system in soccer videos
TL;DR: A novel deep learning approach for 2D ball detection and tracking (DLBT) in soccer videos posing various challenges is presented and yields extraordinary accurate and robust tracking results compared to the other contemporary 2D trackers.
Proceedings ArticleDOI
Convolutional Neural Networks Based Ball Detection in Tennis Games
TL;DR: An innovative deep learning approach to the identification of the ball in tennis context is presented, exploiting the potential of a convolutional neural network classifier to decide whether a ball is being observed in a single frame, overcoming the typical issues that can occur dealing with classical approaches on long video sequences.
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