6,530 research outputs found
An audio-based sports video segmentation and event detection algorithm
In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection. This decision process eliminated the need for defining a heuristic set of rules for segmentation. Each audio segment is then labelled using a series of Hidden Markov model (HMM) classifiers, each a representation of one of 6 predefined semantic content classes found in Soccer video. Exciting events are identified as those segments belonging to a crowd cheering class. Experimentation indicated that the algorithm was more effective for classifying crowd response when compared to traditional model-based segmentation and classification techniques
Towards Commentary-Driven Soccer Player Analytics
Open information extraction (open IE) has been shown to be useful in a number of NLP Tasks, such as question answering, relation extraction, and information retrieval. Soccer is the most watched sport in the world. The dynamic nature of the game corresponds to the team strategy and individual contribution, which are the deciding factors for a team’s success. Generally, companies collect sports event data manually and very rarely they allow free-access to these data by third parties. However, a large amount of data is available freely on various social media platforms where different types of users discuss these very events. To rely on expert data, we are currently using the live-match commentary as our rich and unexplored data-source.
Our aim out of this commentary analysis is to initially extract key events from each game and eventually key entities like players involved, player action and other player related attributes from these key events. We propose an end-to-end application to extract commentaries and extract player attributes from it. The study will primarily depend on an extensive crowd labelling of data involving precautionary periodical checks to prevent incorrectly tagged data. This research will contribute significantly towards analysis of commentary and acts as a cheap tool providing player performance analysis for smaller to intermediate budget soccer club
Going for GOAL: A Resource for Grounded Football Commentaries
Recent video+language datasets cover domains where the interaction is highly
structured, such as instructional videos, or where the interaction is scripted,
such as TV shows. Both of these properties can lead to spurious cues to be
exploited by models rather than learning to ground language. In this paper, we
present GrOunded footbAlL commentaries (GOAL), a novel dataset of football (or
`soccer') highlights videos with transcribed live commentaries in English. As
the course of a game is unpredictable, so are commentaries, which makes them a
unique resource to investigate dynamic language grounding. We also provide
state-of-the-art baselines for the following tasks: frame reordering, moment
retrieval, live commentary retrieval and play-by-play live commentary
generation. Results show that SOTA models perform reasonably well in most
tasks. We discuss the implications of these results and suggest new tasks for
which GOAL can be used. Our codebase is available at:
https://gitlab.com/grounded-sport-convai/goal-baselines.Comment: Preprint formatted using the ACM Multimedia template (8 pages +
appendix
Storia: Summarizing Social Media Content based on Narrative Theory using Crowdsourcing
People from all over the world use social media to share thoughts and
opinions about events, and understanding what people say through these channels
has been of increasing interest to researchers, journalists, and marketers
alike. However, while automatically generated summaries enable people to
consume large amounts of data efficiently, they do not provide the context
needed for a viewer to fully understand an event. Narrative structure can
provide templates for the order and manner in which this data is presented to
create stories that are oriented around narrative elements rather than
summaries made up of facts. In this paper, we use narrative theory as a
framework for identifying the links between social media content. To do this,
we designed crowdsourcing tasks to generate summaries of events based on
commonly used narrative templates. In a controlled study, for certain types of
events, people were more emotionally engaged with stories created with
narrative structure and were also more likely to recommend them to others
compared to summaries created without narrative structure
Video Highlight Prediction Using Audience Chat Reactions
Sports channel video portals offer an exciting domain for research on
multimodal, multilingual analysis. We present methods addressing the problem of
automatic video highlight prediction based on joint visual features and textual
analysis of the real-world audience discourse with complex slang, in both
English and traditional Chinese. We present a novel dataset based on League of
Legends championships recorded from North American and Taiwanese Twitch.tv
channels (will be released for further research), and demonstrate strong
results on these using multimodal, character-level CNN-RNN model architectures.Comment: EMNLP 201
SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries
Soccer is more than just a game - it is a passion that transcends borders and
unites people worldwide. From the roar of the crowds to the excitement of the
commentators, every moment of a soccer match is a thrill. Yet, with so many
games happening simultaneously, fans cannot watch them all live. Notifications
for main actions can help, but lack the engagement of live commentary, leaving
fans feeling disconnected. To fulfill this need, we propose in this paper a
novel task of dense video captioning focusing on the generation of textual
commentaries anchored with single timestamps. To support this task, we
additionally present a challenging dataset consisting of almost 37k timestamped
commentaries across 715.9 hours of soccer broadcast videos. Additionally, we
propose a first benchmark and baseline for this task, highlighting the
difficulty of temporally anchoring commentaries yet showing the capacity to
generate meaningful commentaries. By providing broadcasters with a tool to
summarize the content of their video with the same level of engagement as a
live game, our method could help satisfy the needs of the numerous fans who
follow their team but cannot necessarily watch the live game. We believe our
method has the potential to enhance the accessibility and understanding of
soccer content for a wider audience, bringing the excitement of the game to
more people
Building Scalable Video Understanding Benchmarks through Sports
Existing benchmarks for evaluating long video understanding falls short on
two critical aspects, either lacking in scale or quality of annotations. These
limitations arise from the difficulty in collecting dense annotations for long
videos, which often require manually labeling each frame. In this work, we
introduce an automated Annotation and Video Stream Alignment Pipeline
(abbreviated ASAP). We demonstrate the generality of ASAP by aligning unlabeled
videos of four different sports with corresponding freely available dense web
annotations (i.e. commentary). We then leverage ASAP scalability to create
LCric, a large-scale long video understanding benchmark, with over 1000 hours
of densely annotated long Cricket videos (with an average sample length of ~50
mins) collected at virtually zero annotation cost. We benchmark and analyze
state-of-the-art video understanding models on LCric through a large set of
compositional multi-choice and regression queries. We establish a human
baseline that indicates significant room for new research to explore. Our human
studies indicate that ASAP can align videos and annotations with high fidelity,
precision, and speed. The dataset along with the code for ASAP and baselines
can be accessed here: https://asap-benchmark.github.io/
The applications of social media in sports marketing
n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these "footprints" can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed
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