Smart Director: An Event-Driven Directing System for Live Broadcasting

2021 ◽  
Vol 17 (4) ◽  
pp. 1-18
Author(s):  
Yingwei Pan ◽  
Yue Chen ◽  
Qian Bao ◽  
Ning Zhang ◽  
Ting Yao ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Video Broadcasting ◽  
Human In The Loop ◽  
Sports Events ◽  
Subjective Evaluations ◽  
Event Driven ◽  
Visual Importance ◽  
Highlight Detection ◽  
Joint Event ◽  
Sports Broadcasting

Live video broadcasting normally requires a multitude of skills and expertise with domain knowledge to enable multi-camera productions. As the number of cameras keeps increasing, directing a live sports broadcast has now become more complicated and challenging than ever before. The broadcast directors need to be much more concentrated, responsive, and knowledgeable, during the production. To relieve the directors from their intensive efforts, we develop an innovative automated sports broadcast directing system, called Smart Director, which aims at mimicking the typical human-in-the-loop broadcasting process to automatically create near-professional broadcasting programs in real-time by using a set of advanced multi-view video analysis algorithms. Inspired by the so-called “three-event” construction of sports broadcast [ 14 ], we build our system with an event-driven pipeline consisting of three consecutive novel components: (1) the Multi-View Event Localization to detect events by modeling multi-view correlations, (2) the Multi-View Highlight Detection to rank camera views by the visual importance for view selection, and (3) the Auto-Broadcasting Scheduler to control the production of broadcasting videos. To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events. It is also the first system to solve the novel problem of multi-view joint event detection by cross-view relation modeling. We conduct both objective and subjective evaluations on a real-world multi-camera soccer dataset, which demonstrate the quality of our auto-generated videos is comparable to that of the human-directed videos. Thanks to its faster response, our system is able to capture more fast-passing and short-duration events which are usually missed by human directors.

Workflows Científicos com Apoio de Bases de Conhecimento em Tempo Real

2020 ◽  
Author(s):  
Victor S. Bursztyn ◽  
Jonas Dias ◽  
Marta Mattoso
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Large Scale ◽  
Workflow Engine ◽  
Workflow Execution ◽  
Human In The Loop ◽  
Domain Specific ◽  
Political Sciences ◽  
Provenance Data ◽  
Domain Specific Knowledge

One major challenge in large-scale experiments is the analytical capacity to contrast ongoing results with domain knowledge. We approach this challenge by constructing a domain-specific knowledge base, which is queried during workflow execution. We introduce K-Chiron, an integrated solution that combines a state-of-the-art automatic knowledge base construction (KBC) system to Chiron, a well-established workflow engine. In this work we experiment in the context of Political Sciences to show how KBC may be used to improve human-in-the-loop (HIL) support in scientific experiments. While HIL in traditional domain expert supervision is done offline, in K-Chiron it is done online, i.e. at runtime. We achieve results in less laborious ways, to the point of enabling a breed of experiments that could be unfeasible with traditional HIL. Finally, we show how provenance data could be leveraged with KBC to enable further experimentation in more dynamic settings.

scholarly journals The Power of the Local in Sports Broadcasting: A Cross-Cultural Analysis of Rugby Commentary

2009 ◽  
Vol 2 (2) ◽  
pp. 129-145 ◽  
Author(s):  
Fabrice Desmarais ◽  
Toni Bruce
Keyword(s):  
New Zealand ◽  
Rugby Union ◽  
Cultural Analysis ◽  
Cross Cultural ◽  
Cultural Contexts ◽  
Sports Events ◽  
Local Pressures ◽  
Sports Broadcasting ◽  
Cross Cultural Analysis

This article explores how local pressures intersect to produce differing broadcasts in 2 cultural contexts. This is achieved via a cross-cultural analysis of a decade of televised rugby union matches between France and New Zealand and interviews with leading commentators in both countries. The authors argue that although the overarching commercial imperative to capture audiences might be the same in both countries, and despite global tendencies toward homogenized presentation of sports events, there are local differences in expectations about which kinds of audiences should be captured, and these lead to different practices and emphases in the live broadcasts. The authors suggest that in each country, broadcasts are the result of a complex set of pressures that interact to produce broadcasts with “local” flavor and characteristics.

scholarly journals Human-in-the-loop: Role in Cyber Physical Agricultural Systems

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Maitreya Sreeram ◽  
Shimon Y. Nof
Keyword(s):  
System Performance ◽  
Domain Knowledge ◽  
Performance Metrics ◽  
Human Perception ◽  
Uncertain Environments ◽  
Human In The Loop ◽  
Human Element ◽  
Diagnostic Skills ◽  
Complete Automation ◽  
Collaborative Platform

With increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. Complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. Leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “Human-in-the-loop” (HITL) systems are systems which incorporate meaningful human interactions into the workflow. Agricultural Robotic Systems (ARS), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where HITL augmentation can provide improved detection capabilities and system performance. Humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. Owing to the multi-agent nature of ARS, HUB-CI, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. The challenge remains in designing and integrating human roles and tasks in the automated loop. This article explains the development of a HITL simulation for ARS, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. The results show the statistically significant advantages of HUB-CI protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. Strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a HITL-ARS.

Operator 4.0 Within the Framework of Industry 4.0

2021 ◽  
pp. 398-410
Author(s):  
Sarbjeet Singh ◽  
Phillip Tretten
Keyword(s):  
Industry 4.0 ◽  
Domain Knowledge ◽  
Cyber Physical Systems ◽  
Automated Systems ◽  
Physical Systems ◽  
Human In The Loop ◽  
Intelligent Machines ◽  
Skilled Operator ◽  
Technical Systems ◽  
New Generation

Operator 4.0 is a smart and skilled operator who augments the symbiosis between intelligent machines and operators. Better integration of Operator 4.0 in Industry 4.0 can bring emphasis on human-centric approach, allowing for a paradigm shift towards a human-automation cooperation for inspiring the compulsion of human-in-the-loop. This further enhances the domain knowledge for the improvement of human cyber-physical systems for new generation automated systems. This cooperation of humans and automation makes stability in socio-technical systems with smart automation and human-machine interfacing technologies. This chapter discusses the design principles of Industry 4.0 and Operator 4.0 human-cyber physical systems.

Operator 4.0 Within the Framework of Industry 4.0

2020 ◽  
pp. 120-133
Author(s):  
Sarbjeet Singh ◽  
Phillip Tretten
Keyword(s):  
Industry 4.0 ◽  
Domain Knowledge ◽  
Cyber Physical Systems ◽  
Automated Systems ◽  
Physical Systems ◽  
Human In The Loop ◽  
Intelligent Machines ◽  
Skilled Operator ◽  
Technical Systems ◽  
New Generation

Operator 4.0 is a smart and skilled operator who augments the symbiosis between intelligent machines and operators. Better integration of Operator 4.0 in Industry 4.0 can bring emphasis on human-centric approach, allowing for a paradigm shift towards a human-automation cooperation for inspiring the compulsion of human-in-the-loop. This further enhances the domain knowledge for the improvement of human cyber-physical systems for new generation automated systems. This cooperation of humans and automation makes stability in socio-technical systems with smart automation and human-machine interfacing technologies. This chapter discusses the design principles of Industry 4.0 and Operator 4.0 human-cyber physical systems.

scholarly journals Sports broadcasting, journalism and the challenge of new media

2012 ◽  
Vol 28 (53) ◽  
pp. 17
Author(s):  
Kirsten Frandsen
Keyword(s):  
New Media ◽  
Digital Media ◽  
Digital Platforms ◽  
Digital Platform ◽  
Sports Events ◽  
Sports Broadcasting

<p>This article explores the challenge faced by established media organisations integrating digital media in their production. Using a case study of a Danish broadcaster’s use of blogs in their coverage of major sports events, it is argued that the challenge is strategic in a broader sense, as the move to digital platforms is influenced by economic, organisational as well as conceptual parameters for roles. It is argued that in order to understand the potential and challenges of this case, the peculiarities of the role of sports journalists in broadcasting have to be taken into consideration. The case illustrates how their distinctive engagement with their topic and the audience makes some of them more prone to work for pleasure and produce for the digital platform on very unclear conditions, just as it influences the interaction that takes place in the blogs in various ways.</p>

scholarly journals Workflows Científicos com Apoio de Bases de Conhecimento em Tempo Real

2018 ◽  
Author(s):  
Victor S. Bursztyn ◽  
Jonas Dias ◽  
Marta Mattoso
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Large Scale ◽  
Workflow Engine ◽  
Workflow Execution ◽  
Human In The Loop ◽  
Domain Specific ◽  
Political Sciences ◽  
Provenance Data ◽  
Domain Specific Knowledge

One major challenge in large-scale experiments is the analytical capacity to contrast ongoing results with domain knowledge. We approach this challenge by constructing a domain-specific knowledge base, which is queried during workflow execution. We introduce K-Chiron, an integrated solution that combines a state-of-the-art automatic knowledge base construction (KBC) system to Chiron, a well-established workflow engine. In this work we experiment in the context of Political Sciences to show how KBC may be used to improve human-in-the-loop (HIL) support in scientific experiments. While HIL in traditional domain expert supervision is done offline, in K-Chiron it is done online, i.e. at runtime. We achieve results in less laborious ways, to the point of enabling a breed of experiments that could be unfeasible with traditional HIL. Finally, we show how provenance data could be leveraged with KBC to enable further experimentation in more dynamic settings.  

scholarly journals REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

2021 ◽  
Author(s):  
Zohreh Shams ◽  
Botty Dimanov ◽  
Sumaiyah Kola ◽  
Nikola Simidjievski ◽  
Helena Andres Terre ◽  
...  
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Domain Knowledge ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biological Research ◽  
Cancer Subtypes ◽  
Biologically Relevant ◽  
Human In The Loop

AbstractDeep learning models are receiving increasing attention in clinical decision-making, however the lack of interpretability and explainability impedes their deployment in day-to-day clinical practice. We propose REM, an interpretable and explainable methodology for extracting rules from deep neural networks and combining them with other data-driven and knowledge-driven rules. This allows integrating machine learning and reasoning for investigating applied and basic biological research questions. We evaluate the utility of REM on the predictive tasks of classifying histological and immunohistochemical breast cancer subtypes from genotype and phenotype data. We demonstrate that REM efficiently extracts accurate, comprehensible and, biologically relevant rulesets from deep neural networks that can be readily integrated with rulesets obtained from tree-based approaches. REM provides explanation facilities for predictions and enables the clinicians to validate and calibrate the extracted rulesets with their domain knowledge. With these functionalities, REM caters for a novel and direct human-in-the-loop approach in clinical decision making.

scholarly journals REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

2021 ◽  
Author(s):  
Zohreh Shams ◽  
Botty Dimanov ◽  
Sumaiyah Kola ◽  
Nikola Simidjievski ◽  
Helena Andres Terre ◽  
...  
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Domain Knowledge ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biological Research ◽  
Cancer Subtypes ◽  
Biologically Relevant ◽  
Human In The Loop

AbstractDeep learning models are receiving increasing attention in clinical decision-making, however the lack of interpretability and explainability impedes their deployment in day-to-day clinical practice. We propose REM, an interpretable and explainable methodology for extracting rules from deep neural networks and combining them with other data-driven and knowledge-driven rules. This allows integrating machine learning and reasoning for investigating applied and basic biological research questions. We evaluate the utility of REM on the predictive tasks of classifying histological and immunohistochemical breast cancer subtypes from genotype and phenotype data. We demonstrate that REM efficiently extracts accurate, comprehensible and, biologically relevant rulesets from deep neural networks that can be readily integrated with rulesets obtained from tree-based approaches. REM provides explanation facilities for predictions and enables the clinicians to validate and calibrate the extracted rulesets with their domain knowledge. With these functionalities, REM caters for a novel and direct human-in-the-loop approach in clinical decision making.

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