The development of computer vision technologies has significantly transformed the way we work and lead our everyday lives. From content analysis and categorization to audience behavior analysis, computer vision software interprets vast amounts of data every day. In this article, we’ll discuss computer vision applications in media and sports analytics as well as how different emerging challenges of these applications can be tackled.

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Computer Vision in Media Analytics

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Computer vision technologies are present in various aspects of media, from content analysis and brand monitoring to evaluating how audiences respond to and engage with content and ads. 

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Content Analysis and Categorization

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First and foremost, computer vision technology utilizes machine learning algorithms to analyze content in the form of textual data or visual data such as photos and videos and make conclusions based on the extracted data. For example, media news outlets often have multiple sources of information that they need to monitor and interpret daily, which can be resource-consuming in terms of time and human capital. By using computer vision they can not only analyze but also extract and categorize the relevant information. Moreover, they can also identify recurring trends and themes, which can also be beneficial for their marketing and promotion strategy. 

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For instance, journalists have to consume vast amounts of visual data and text daily to identify recurring topics and events. A notable example of how journalists have used computer vision technology is the case of Implant Files investigation conducted by over 250 journalists from the International Consortium of Investigative Journalists (ICIJ). They used computer vision algorithms to analyze reports sent to the U.S. Food and Drug Administration. As a result, they uncovered cases of 220 patient deaths, which were misclassified as injuries or malfunctions, but in reality, occurred due to medical device failure. 

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Engagement Tracking 

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Computer vision can be employed to track and analyze human interaction with visual content. This process may include gaze detection, emotion detection, facial expressions, and body language analysis. In marketing, advertising, and media, this information is valuable for enhancing content and getting better user engagement. An example of emotion tracking software is Emotion AI, which uses computer vision to analyze viewers’ facial expressions and understand their emotional response. Paired with more traditional marketing metrics like watch time and click-through rate, it can provide insights that can change strategies of marketing companies. 

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Ad Response Analysis

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Marketers often rely on best practices, surveys, and even intuition for creating ads that will best serve their advertising goals. This approach is an unreliable strategy as human bias may affect the way best practices are determined based on experience and audience surveys. Therefore, as AI technology evolves, computer vision offers better strategies for creating ad creatives and content. Similar to engagement tracking, computer vision systems can detect subtle cues like eye movement, facial expressions, and gestures to better understand audience response to ad visuals. 

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Brand Monitoring

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The amount of data generated every day is increasing at a rapid pace every year. As a consequence, it’s getting more difficult for brands to monitor their online presence. This is when computer vision comes into play. Technologies like logo detection and recognition can help brands monitor where and how their brand is represented online. If necessary, they will be able to make decisions to protect their brand reputation. 

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Additionally, computer vision enables identifying potential trademark infringements, counterfeit products, or unauthorized brand usage, helping companies maintain a positive brand image. For instance, tech giant company Microsoft has created Azure AI Vision, which, among its many use cases, can also be employed for brand detection purposes. Using a large database with thousands of logos used worldwide, it can identify a brand name along with a confidence score, as well as can evaluate brand popularity on social media. 

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Sentiment Evaluation

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Similarly, sentiment evaluation can be used to analyze audience responses to different types of media and advertisements. For example, machine learning algorithms can be used for emotion detection and intent analysis of content such as blogs, forums, comments, etc. As a result, companies can collect feedback and make changes in their operations based on this information. 

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Computer Vision in Sports Analytics

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As in media analytics, computer vision algorithms can fundamentally enhance sports analytics, including the areas of athlete performance and injury prevention, as well as fan behavior analysis and improvement. 

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Player and Ball Tracking

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In the past, in sports competitions, special juries and human monitors have conducted player and ball tracking. Understandably, people are prone to errors and mistakes, which can highly affect the outcome of these competitions. Nowadays, computer vision software can analyze real-time video footage and track the movement of players and the ball with remarkable precision. This capability enables sports analysts, coaches, and fans to gain a deeper understanding of strategic plays, player positioning, and overall game flow. Moreover, it can enhance sports broadcast and post-game analysis, as well as provide essential feedback for player development and coaching strategies. For example, in the 2022 World Cup, FIFA utilized a semi-automated offside technology for their video assistant referee (VAR) system, which resulted in more accurate game officiating by referees. 

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Pose Estimation for Injury Prevention

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Besides player and ball tracking, computer vision is also capable of assisting injury prevention. By analyzing video footage, it can accurately track athletes' movements and detect potential biomechanical abnormalities or risky postures that could lead to injuries. Additionally, it can not only help during live games, but also during practice sessions and games. As a result, trainers and coaches can optimize their training programs, while athletes can improve their techniques. 

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Fan Behavior Analysis

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Similar to sentiment analysis for media, computer vision systems can analyze footage from stadiums and related social media content to analyze fan behavior. By understanding fan behavior, sports organizations and media outlets can tailor their content, marketing strategies, and event experiences to better resonate with their audience. A similar technology was created by IBM for the Wimbledon tennis tournament in 2023. The AI commentary tool provided audio commentary and captions during key moments for fans watching the tournament on the Wimbledon website. Fostering strong connections between sports teams, athletes and their loyal supporters is one of the key activities for sports media. 

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Computer Vision Challenges

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Even though computer vision is used heavily in media and sports analytics, there are several challenges in these spheres. Addressing these challenges is crucial for effective performance and accurate results. 

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One significant question that arises is the reliability of computer vision technology itself. Despite its advancements, there are instances where computer vision could fail, raising doubts about its dependability. Factors such as insufficient training data, biased datasets, complex environmental conditions, and occlusions can all contribute to the failure of computer vision systems. 

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Manot insights management platform can help overcome these challenges. Manot excels in refining and optimizing computer vision models for media and sports analytics challenges, tackling key areas for accurate and effective performance. 

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In media analytics, Manot addresses the issue of low-resolution media by evaluating model performance across varying lighting conditions, ensuring precise threat detection and activity recognition even in challenging visual environments. It also excels in assessing models' effectiveness in handling rapid scene changes and varied lighting and contrast, which is vital for seamless continuity tracking and accurate object and emotion identification. In addition, it also addresses challenges such as overlapping audio-visual elements by examining model accuracy in isolating and analyzing features, ensuring precise classification even in cluttered or noisy environments. This capability is crucial for effective surveillance across diverse activity levels, enhancing the reliability of computer vision systems in complex scenarios.

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In sports analytics, Manot plays a pivotal role in enhancing computer vision performance by refining model accuracy in dynamic sporting environments. It tackles challenges such as varied game conditions, fast-paced moments, and player interactions and obstructions, ensuring effective analysis of player movements and strategies while preventing injuries during close interactions. Furthermore, Manot's ability to differentiate between players wearing similar uniforms and analyze audience engagement behaviors improves tracking and analysis precision. This feature further enhances fan experiences and refines engagement strategies. With its comprehensive capabilities, Manot emerges as a valuable tool in optimizing computer vision performance across diverse applications, ensuring reliability and effectiveness in complex visual environments.

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In summary, the incorporation of computer vision technologies into media and entertainment has fundamentally transformed content analysis, production, and audience engagement. From streamlining content categorization and editing to offering invaluable insights into viewer interaction and fan dynamics, computer vision is driving significant advancements in media and sports analytics. Even though AI and computer vision in particular are growing so fast and being used heavily the quality remains a challenge. Therefore, solutions like Mnaot are crucial to making sure that we have accurate AI and our expectations are met in these vertical-use cases.

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