#Japan #Tokyo #Qlean Dataset #Visual Bank #AI Data Recipe

Enhancing AI Development with Qlean Dataset

Visual Bank, a startup based in Minato-ku, Tokyo, is making significant strides in the field of AI by launching an innovative dataset aimed at sports professionals. Through its subsidiary, Amana Images, Visual Bank has introduced the Giant Slalom Skiing Video and Image Dataset, which serves as part of their comprehensive AI learning data solution known as the Qlean Dataset. This new addition aims to expand the available resources for AI development, particularly in the sports domain.

What is Qlean Dataset?

The Qlean Dataset is a collection of high-quality, commercially viable data tailored for diverse AI applications. The dataset provides flexibility in usage, allowing users to combine data based on specific needs regarding accuracy and delivery times. It also accommodates custom modifications and extensions according to unique project requirements.

Visual Bank's AI Data Recipe offers original data sets for commercial use, showcasing a robust approach to agile data management and rapid deployment for AI developers. Partnerships with well-known organizations like the Chiba Lotte Marines and Toyo Keizai, alongside continuous improvements through new recordings, enhance the overall quality and scope of the datasets.

Overview of the Giant Slalom Skiing Dataset

The newly launched dataset features both video and image content focused on male and female skiers performing a Giant Slalom.

Common Details for Video and Image Dataset

• - Content: The dataset includes footage featuring Japanese skiers, specifically one male and one female, engaging in Giant Slalom practices.
• - Filming Environment: Captured in an indoor ski resort setting with dual-angle filming.

Video Dataset Specs

• - Size: 2.65GB
• - Number of Files: 20
• - Format: MP4
• - Duration: 13 minutes and 39 seconds
• - Sample Link: Video Sample

Image Dataset Specs

• - Size: 11.06GB
• - Number of Files: 3,232
• - Format: JPEG
• - Sample Link: Image Sample

Use Cases for the Giant Slalom Dataset

1. Sports Form Analysis and Pose Estimation: The dataset is optimal for training AI models that analyze the body's center of gravity movements and turning postures during skiing, enhancing sports science research and coaching.

2. Object Tracking and Route Recognition: The stable video data of fast-moving athletes supports the development of AI in object detection and path analysis, perfect for improving recognition accuracy in outdoor settings, including varying weather conditions.

3. Competition Scoring and Training Support: Insights about turn angles, skiing trajectories, and body axis shifts can aid in building quantitative evaluation models for athlete training and assessment in competitions, facilitating effective feedback systems.

4. VR/AR Skiing Experience and Educational Simulations: The multi-angle footage is ideal for recreating skiing experiences in VR/AR environments, making it useful for sports education and tourism applications.

5. Robotics and Balance Control AI Development: Data on body balance and posture transitions during skiing can be pivotal for AI research in robotic movements and control strategies in uneven terrains.

Academia Support Initiative

Visual Bank is committed to supporting academia through the Qlean Dataset, offering a free dataset program tailored to universities and non-profit technical development teams. This initiative grants access to over 80 data types exceeding 500,000 quality data points for academic use, addressing the challenge of obtaining high-quality learning data in research environments. More information is available at Qlean Dataset Academia Program.

About Visual Bank

Founded with a mission to unleash the potential of all data, Visual Bank structures next-generation data infrastructure designed to maximize AI development capabilities. Their service offerings include THE PEN, an AI-assisted tool for manga artists, alongside the Qlean Dataset through their subsidiary, Amana Images. The firm has also been chosen by the governmental R&D program