#Japan #Tokyo #AI Development #Qlean Dataset #Visual Bank

Visual Bank's Innovative Soccer Match Video Dataset

Visual Bank, based in Shibuya, Tokyo, is making strides in AI development by expanding its original data solutions through its subsidiary, Amana Images. The latest addition to its lineup is the soccer match video dataset, part of the well-known Qlean Dataset. This innovative resource aims to support various research and commercial AI projects, facilitating insights into sports analysis and much more.

Overview of Qlean Dataset and Data Recipes

The Qlean Dataset is distinctively known for its commercial viability, offering a range of original data that can be utilized for different AI applications. A core feature of this dataset is the flexibility to combine readily available data materials based on specific needs, such as accuracy and delivery timeline. It supports both annotated and unannotated data, allowing customization and expansion according to individual requirements.

In collaboration with notable partners like Chiba Lotte Marines and Toyo Keizai Inc., Visual Bank is actively improving its dataset offerings. This collaborative effort significantly reduces the data collection and organization workload in AI development, ultimately accelerating progress in the field.

New Soccer Match Video Dataset Features

The new soccer match video dataset includes:

• - Data Type: Video
• - Subject Attributes: Eight males aged 10 to 40
• - Data Size: 35.48GB
• - Total Number of Clips: 28
• - Format: MP4
• - Filming Duration: 4 hours, 11 minutes, and 56 seconds
• - Filming Environment: Soccer field
• - Filming Angles: Captured from four fixed points (A/B/C/D) on the soccer field.

Sample Details: View Sample Dataset

Use Cases of the Soccer Match Video Dataset

The dataset presents numerous applications, particularly in sports analytics:

1. Tactical Analysis and Formation Development: By utilizing footage captured from multiple angles, AI models can automatically analyze player passing routes and formation changes. This can lead to deeper insights into game strategies and tactical understanding.

2. Player Motion Detection and Recognition: The dataset allows for high-accuracy learning of fundamental movements like dribbling, shooting, passing, and defending. It can serve as foundational data for sports analytics AI, aiding in performance assessment and motion feature extraction for individual players.

3. Automated Editing for Broadcasting: With multi-camera recordings, AI models can be developed to perform highlight extraction and automatic digest generation. This improvement aids in the efficiency of sports broadcasting and streaming processes.

4. Judicial Support and Fair Play Detection: Research on AI models to detect scenes of offsides or fouls can significantly improve the consistency of rulings and help prevent incorrect judgments, ultimately supporting better referee systems.

5. Enhancing Autofocus Accuracy: Training models on high-speed player movement footage can enhance autofocus capabilities in AI cameras and smart devices. This is particularly useful in situations involving multiple subjects within the same frame.

6. Virtual Reality Training Simulations: By creating VR environments based on match footage, a training space that mimics real-game scenarios can be established. This offers immersive simulations to enhance player judgment and reaction speed through AI educational tools.

7. Augmented Reality Experience for Spectators: Integrating video analysis with AR glasses or applications allows for real-time overlay of player movement data and tactical information during matches, thus enhancing the future spectator experience and sports tech services.

8. Generic Research on Behavioral Recognition Models: Beyond soccer, this dataset can be utilized for training general AI models focusing on human detection and action recognition, broadening its application for academic and foundational technology research.

Features of Qlean Dataset

• - Research and Commercial Use: All subjects used in the Qlean Dataset have consent for data capture and use, adhering to privacy policies in various countries, ensuring safe use for both research and commercial purposes.

• - Rapid Provisioning of Data Recipes: The unique data recipe model of the Qlean Dataset enables swift procurement with reduced initial investments.

• - Customization for Unique Dataset Needs: Datasets not available in the data recipes can be individually customized and built according to specific requirements, leveraging the capabilities of Qlean Dataset.

Academia Support Program

In a significant initiative, Visual Bank has launched a free dataset provision program under Qlean Dataset to support academia. Universities, research institutions, and non-profit tech development teams can access over 500,000 data points across various types such as images, audio, video, and text. This effort aims to resolve the common issue of securing high-quality and rights-cleared learning data in research settings, showcasing Visual Bank's commitment to innovation and support in AI learning environments.

Conclusion

As Visual Bank continues to enhance its offerings in the AI data landscape, the new soccer match video dataset stands as a testament to its commitment to pushing the boundaries of AI application in sports and beyond. With a focus on research, collaboration, and innovation, Qlean Dataset is setting a new standard for data solutions in the field of artificial intelligence.