540 research outputs found
Tracking a table tennis ball for umpiring purposes
This study investigates tracking a table-tennis ball rapidly from video captured using low-cost equipment for umpiring purposes. A number of highly efficient algorithms have been developed for this purpose. The proposed system was tested using sequences capture from real match scenes. The preliminary results of experiments show that accurate and rapid tracking can be achieved even under challenging conditions, including occlusion and colour merging. This work can contribute to the development of an automatic umpiring system and also has the potential to provide amateur users open access to a detection tool for fast-moving, small, round objects
Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM
As artificial intelligence spreads out to numerous fields, the application of
AI to sports analytics is also in the spotlight. However, one of the major
challenges is the difficulty of automated acquisition of continuous movement
data during sports matches. In particular, it is a conundrum to reliably track
a tiny ball on a wide soccer pitch with obstacles such as occlusion and
imitations. Tackling the problem, this paper proposes an inference framework of
ball trajectory from player trajectories as a cost-efficient alternative to
ball tracking. We combine Set Transformers to get permutation-invariant and
equivariant representations of the multi-agent contexts with a hierarchical
architecture that intermediately predicts the player ball possession to support
the final trajectory inference. Also, we introduce the reality loss term and
postprocessing to secure the estimated trajectories to be physically realistic.
The experimental results show that our model provides natural and accurate
trajectories as well as admissible player ball possession at the same time.
Lastly, we suggest several practical applications of our framework including
missing trajectory imputation, semi-automated pass annotation, automated
zoom-in for match broadcasting, and calculating possession-wise running
performance metrics
TennisSense: a platform for extracting semantic information from multi-camera tennis data
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
Automated Top View Registration of Broadcast Football Videos
In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
model. Automatic registration using existing approaches has been difficult due
to the lack of sufficient point correspondences. We investigate an alternate
approach exploiting the edge information from the line markings on the field.
We formulate the registration problem as a nearest neighbour search over a
synthetically generated dictionary of edge map and homography pairs. The
synthetic dictionary generation allows us to exhaustively cover a wide variety
of camera angles and positions and reduce this problem to a minimal per-frame
edge map matching procedure. We show that the per-frame results can be improved
in videos using an optimization framework for temporal camera stabilization. We
demonstrate the efficacy of our approach by presenting extensive results on a
dataset collected from matches of football World Cup 2014
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