235 research outputs found
A Novel and Effective Short Track Speed Skating Tracking System
This dissertation proposes a novel and effective system for tracking high-speed skaters. A novel registration method is employed to automatically discover key frames to build the panorama. Then, the homography between a frame and the real world rink can be generated accordingly. Aimed at several challenging tracking problems of short track skating, a novel multiple-objects tracking approach is proposed which includes: Gaussian mixture models (GMMs), evolving templates, constrained dynamical model, fuzzy model, multiple templates initialization, and evolution. The outputs of the system include spatialtemporal trajectories, velocity analysis, and 2D reconstruction animations. The tracking accuracy is about 10 cm (2 pixels). Such information is invaluable for sports experts. Experimental results demonstrate the effectiveness and robustness of the proposed system
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
Anomaly Detection, Rule Adaptation and Rule Induction Methodologies in the Context of Automated Sports Video Annotation.
Automated video annotation is a topic of considerable interest in computer vision due to its applications in video search, object based video encoding and enhanced broadcast content. The domain of sport broadcasting is, in particular, the subject of current research attention due to its fixed, rule governed, content. This research work aims to develop, analyze and demonstrate novel methodologies that can be useful in the context of adaptive and automated video annotation systems. In this thesis, we present methodologies for addressing the problems of anomaly detection, rule adaptation and rule induction for court based sports such as tennis and badminton. We first introduce an HMM induction strategy for a court-model based method that uses the court structure in the form of a lattice for two related modalities of singles and doubles tennis to tackle the problems of anomaly detection and rectification. We also introduce another anomaly detection methodology that is based on the disparity between the low-level vision based classifiers and the high-level contextual classifier. Another approach to address the problem of rule adaptation is also proposed that employs Convex hulling of the anomalous states. We also investigate a number of novel hierarchical HMM generating methods for stochastic induction of game rules. These methodologies include, Cartesian product Label-based Hierarchical Bottom-up Clustering (CLHBC) that employs prior information within the label structures. A new constrained variant of the classical Chinese Restaurant Process (CRP) is also introduced that is relevant to sports games. We also propose two hybrid methodologies in this context and a comparative analysis is made against the flat Markov model. We also show that these methods are also generalizable to other rule based environments
Extracting field hockey player coordinates using a single wide-angle camera
In elite level sport, coaches are always trying to develop tactics to better their
opposition. In a team sport such as field hockey, a coach must consider both the
strengths and weaknesses of both their own team and that of the opposition to
develop an effective tactic. Previous work has shown that spatiotemporal coordinates
of the players are a good indicator of team performance, yet the manual extraction of
player coordinates is a laborious process that is impractical for a performance analyst.
Subsequently, the key motivation of this work was to use a single camera to capture
two-dimensional position information for all players on a field hockey pitch.
The study developed an algorithm to automatically extract the coordinates of the
players on a field hockey pitch using a single wide-angle camera. This is a non-trivial
problem that requires: 1. Segmentation and classification of a set of players that are
relatively small compared to the image size, and 2. Transformation from image
coordinates to world coordinates, considering the effects of the lens distortion due to
the wide-angle lens. Subsequently the algorithm addressed these two points in two
sub-algorithms: Player Feature Extraction and Reconstruct World Points.
Player Feature Extraction used background subtraction to segment player blob
candidates in the frame. 61% of blobs in the dataset were correctly segmented, while a
further 15% were over-segmented. Subsequently a Convolutional Neural Network was
trained to classify the contents of blobs. The classification accuracy on the test set was
85.9%. This was used to eliminate non-player blobs and reform over-segmented blobs.
The Reconstruct World Points sub-algorithm transformed the image coordinates into
world coordinates. To do so the intrinsic and extrinsic parameters were estimated
using planar camera calibration. Traditionally the extrinsic parameters are optimised
by minimising the projection error of a set of control points; it was shown that this
calibration method is sub-optimal due to the extreme camera pose. Instead the
extrinsic parameters were estimated by minimising the world reconstruction error. For
a 1:100 scale model the median reconstruction error was 0.0043 m and the
distribution of errors had an interquartile range of 0.0025 m. The Acceptable Error
Rate, the percentage of points that were reconstructed with less than 0.005 m of
error, was found to be 63.5%.
The overall accuracy of the algorithm was assessed using the precision and the recall. It
found that players could be extracted within 1 m of their ground truth coordinates
with a precision of 75% and a recall of 66%. This is a respective improvement of 20%
and 16% improvement on the state-of-the-art. However it also found that the
likelihood of extraction decreases the further a player is from the camera, reducing to
close to zero in parts of the pitch furthest from the camera. These results suggest that
the developed algorithm is unsuitable to identify player coordinates in the extreme
regions of a full field hockey pitch; however this limitation may be overcome by using
multiple collocated cameras focussed on different regions of the pitch. Equally, the
algorithm is sport agnostic, so could be used in a sport that uses a smaller pitch
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