348 research outputs found
Indirect Match Highlights Detection with Deep Convolutional Neural Networks
Highlights in a sport video are usually referred as actions that stimulate
excitement or attract attention of the audience. A big effort is spent in
designing techniques which find automatically highlights, in order to
automatize the otherwise manual editing process. Most of the state-of-the-art
approaches try to solve the problem by training a classifier using the
information extracted on the tv-like framing of players playing on the game
pitch, learning to detect game actions which are labeled by human observers
according to their perception of highlight. Obviously, this is a long and
expensive work. In this paper, we reverse the paradigm: instead of looking at
the gameplay, inferring what could be exciting for the audience, we directly
analyze the audience behavior, which we assume is triggered by events happening
during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to
extract visual features from cropped video recordings of the supporters that
are attending the event. Outputs of the crops belonging to the same frame are
then accumulated to produce a value indicating the Highlight Likelihood (HL)
which is then used to discriminate between positive (i.e. when a highlight
occurs) and negative samples (i.e. standard play or time-outs). Experimental
results on a public dataset of ice-hockey matches demonstrate the effectiveness
of our method and promote further research in this new exciting direction.Comment: "Social Signal Processing and Beyond" workshop, in conjunction with
ICIAP 201
Automatic annotation of tennis games: An integration of audio, vision, and learning
Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level
Initial pattern library algorithm for human action recognition
AbstractHuman action recognition is currently one of the most active research topics in society management, including human moving detection, human moving classification, human moving tracking, and activity recognition and description. In this paper, we have proposed a new classifying and sorting initial pattern library algorithm for human action recognition. First, we classify the training vector set to two subsets by vector variance. Secondly, sort the subsets to put the similar pattern vectors together. Last, select some number of pattern vectors from the sorted subsets to form the initial pattern library. This new initial pattern library is tested by self-organizing maps (SOM) algorithm. Experimental results in image recognition show that this new initial pattern library algorithm is better than the common random sampling initial pattern library
Automatic annotation of tennis games: an integration of audio, vision, and learning
Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level
Automatic annotation of tennis games: an integration of audio, vision, and learning
Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level
Social Media: How Players and Athletic Organizations Can Use Social Media Technology for Positive Brand Awareness
Player transgressions and sponsorships were examined lightly using rhetorical and secondary research to consider the impact social media technology has had on sports marketing and communications. In discussing the impact, I was able to uncover the negative and positive implications for social media‟s involvement in sports, and how understanding it is imperative for individuals looking to become professionals in athletics. From my research, I will create a presentation which will assist professional athletes and their organization in creating a positive brand image using social media technology
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
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