Sports
Keylabs: driving sports AI forward with precision annotation.
In recent years, technology has revolutionized the way we approach sports. From AI referees to match analytics and fitness tracking, technology has made it easier for athletes and coaches to improve their performance. One of the most exciting developments in this field is AI coaching, which uses data to provide personalized training and feedback to athletes. However, AI coaching is only as good as the data it is based on.
This is where Keylabs comes in. Keylabs is a data annotation platform that provides high-quality labeled data for a variety of applications, including sports. With Keylabs, sports teams and organizations can access accurate and reliable data for AI coaching, match analytics, fitness tracking, and more.
Data annotation tools
Some common applications of sports AI include:
AI referee
KeyLabs is a top-tier data annotation platform that prepares visual data for machine learning. The platform is equipped with advanced annotation tools, including keypoint skeleton. Using this tool, it's possible to identify human joints with greater accuracy.
Overall, KeyLabs' extensive capabilities make it an excellent resource for sport applications that require visual data management like improving athletes' performance evaluation through monitoring player forms or developing technology that helps referees keep up with fast-paced games.
Match analytics
Keylabs is an advanced platform designed specifically for preparing visual data to be used in machine learning.
One exciting application for Keylabs is its ability to support sports performance analytics software. With a range of supported annotation types - including cuboid, bounding boxes, segmentation, and keypoint skeleton annotation for human pose estimation - Keylabs can be used to create annotated datasets to feed track key metrics models for almost any sport.
Fitness tracking
The use of fitness mirrors and applications have become increasingly popular in recent years.
Keylabs offers annotation tools such as object tracking to accelerate skeletal annotation, ultimately improving visual data for machine learning algorithms used in sports technology. Our labeling platform can also be adapted for use in fitness tracking apps by providing more advanced methods of analyzing workout routines through image recognition technology.
AI Coaching
Incorporating AI into the sports industry can enhance performance by using predictive analysis and wearable devices that provide valuable information on injury prevention and recovery, allowing athletes to push themselves safely.
Keylabs' proprietary platform for video and image annotations allows object tracking from frame-to-frame improving the accuracy of skeletal annotations while increasing speed of the process.
Keylabs' skeletal annotation system allows for precise labeling of visual data sets from sports events capturing joint angles posture among other variables making it ideal for guiding athletic movements through coaching apps leveraging bone structures.
Key features
There are several key features that a robust data annotation tool for sports should possess:
Versatility
The tool should be capable of handling different types of data, including 2D and 3D images, videos and point clouds generated by LiDAR sensors.
Precision
High-quality annotations are crucial for the accuracy of AI models in aerial management and disaster management. The tool should enable precise labeling of objects and features, minimizing the chances of misinterpretation.
Scalability
A data annotation tool should be scalable to handle large datasets efficiently, streamlining the annotation process and reducing the time required for model training.
Automation
AI-powered data annotation tools can leverage machine learning algorithms to automate parts of the annotation process, speeding up the workflow and increasing overall efficiency.
Collaboration
A good data annotation tool should facilitate collaboration among team members, enabling multiple annotators to work together on the same dataset. This ensures consistency in labeling and accelerates the annotation process.
Quality control
To ensure the highest level of accuracy, the tool should have built-in quality control features that allow for easy review and verification of annotated data. This helps maintain data integrity and improves the overall performance of the AI models being trained.
Customization
The annotation requirements may change or become more complex. A flexible data annotation tool should allow for customization to meet the unique needs of each project and adapt to new challenges in the industry.
Integration
The data annotation tool should be able to integrate seamlessly with various machine learning frameworks and platforms, making it easier for developers to use the annotated data for model training and evaluation.
Data Security
A data annotation tool must prioritize data security and privacy, ensuring that the information is protected at all stages of the annotation process.
Versatility
Precision
Scalability
Automation
Collaboration
Quality Control
Customization
Integration
Data Security
Use cases
AI referee
Keylabs enhances AI refereeing in sports with accurate annotations, ensuring fair and precise decision-making during games.
Match analytics
For match analytics, Keylabs offers detailed annotations that provide deep insights into player performances and game strategies.
Fitness tracking (fitness mirrors & apps)
Keylabs excels in fitness tracking, annotating data for mirrors and apps to offer personalized feedback and track workout progress.
AI coaching
In AI coaching, Keylabs' annotations help create intelligent coaching systems, offering tailored training and performance enhancement.
Starter’s guide
Data security
Using the Keylabs annotation tools comes with a commitment to data safety. Keylabs employs a range of security measures to protect valuable and sensitive data. This includes comprehensive infrastructure security if you choose to access Keylabs through the cloud. Alternatively, Keylabs can be installed on premises, guaranteeing you total control over access. We will continue to emphasize data protections as a priority by utilizing a diverse array of security measures and industry best practices.
Top Features
Keylabs is created as a platform that incorporates state-of-the-art, performance oriented tools and processes.
ML assisted data annotation
ML assisted data annotation
Keylabs is a streamlined data labeling platform with AI-enhanced annotation.
Tailored for easy Integration with any client model and time & cost efficiency.
Keylabs’ advanced algorithms provide quick, accurate data prep for superior model training.
3D tool
3D tool
Keylabs is a super-fast tool, soaring through Lidar files at ultra speeds. It seamlessly handles all file formats, ensuring a consistent, efficient workflow regardless of file complexity.
High performance video annotation
High performance video annotation
With the Keylabs platform's technical and software capabilities, video annotation is highly accurate (precision of up to 99,9% depending on project needs) and fast. Thanks to the geolocation adaptation of servers, even big-sized videos are loaded and processed quickly.
Magic wand
Magic wand
Speeds up the annotation process by automatically detecting closed shapes of the same color or color gradient in a highly precise manner.
Object interpolation
Object interpolation
Object interpolation in the data annotation process is used to accelerate the annotation of objects across a sequence of frames in video annotation.
Annotators label the shape of an object in the first and the last keyframe of desired sequence and the object interpolation algorithm automatically generates the labels for the object in the intermediate frames.
It saves time and also ensures consistent labeling across frames.
A-Z order
A-Z order
Objects can be placed on different leveled layers, which allows operators to correctly detect and work with those objects and their boundaries.
Multilayer annotation
Multilayer annotation
Multilayer annotation is a complex yet valuable process in data annotation where different types of materials are layered onto a single item.
This allows the addition of multiple, diverse annotations to a single piece of data such as an image or video frame.
Each layer might provide a different dimension of information, enriching the dataset with multiple facets of detail.
This allows the addition of multiple, diverse annotations.
Object linking
Object linking
Object linking in the data annotation process is a valuable function that connects different instances of the same object across multiple frames or images.
For example, in video annotation, an object appearing in different frames is linked throughout the video, ensuring the continuity and consistency of the annotation.
Hierarchical atributes
Hierarchical atributes
The attribute is a type of tag that can be applied to a class or property to provide metadata about it.
Using attribute hierarchies, it is possible to define structures of metadata for each item in dataset.
It is achieved by using dependent attributes, which allows logical forming of metadata information for frame or object individually.
Workflow and task distribution
Workflow and task distribution
Workflow includes custom stages of one of 4 project stage types: annotation, verification, miscellanious and final.
Good workflow and task distribution ensure that the data annotation process is smooth, efficient and completed within the required timeframe.
Data management
Data management
Data management in the context of the data annotation process is about strategically handling and organizing the data throughout its lifecycle.
Effective data management helps to uphold data integrity and ensure that the final annotated data is accurate, consistent and ready for use in AI and machine learning projects.
Attributes interpolation
Attributes interpolation
Attributes metadata can also be interpolatable (changed) between the frames. For instance, In a self-driving car video annotation, this can label a car as a "sedan" going "30 mph" in the first frame & automatically estimate its type & speed in subsequent frames until the next key frame. This eliminates the need for manual annotation in each intervening frame, saving time & effort.
Annotation types
Keylabs gives developers access to a full suite of annotation techniques:
Bounding Box
A rectangular box defined by coordinates that encapsulates an object of interest within an image
Oriented bounding box
A rotated rectangle that tightly encloses an object, accommodating its orientation and shape more precisely than a standard bounding box
Polygon
A closed plane figure made up of several line segments that are joined together, used to define irregular shapes in an image
Points
The Point Annotation Tool places dots on images or videos, ideal for highlighting details like facial features, expressions and body postures
Lines & Multilines
A data annotation tool used to draw single or multiple interconnected lines on images, capturing linear features or paths
Skeleton
A thin version of a shape, representing its central structure and providing a simplified representation of its form, commonly used in understanding object morphology or structure
Instance Segmentation
The process of classifying and delineating each individual object instance in an image
Semantic Segmentation
The classification of each pixel in an image based on its semantic category, without distinguishing between individual object instances
Bitmask
A binary representation where each pixel value indicates whether it belongs to the object (1) or the background (0)
Cuboid
A 3D rectangular prism annotation, often used to represent objects in spatial dimensions
Mesh
A collection of vertices, edges and faces that define the shape of a 3D object in space, often used in 3D modeling and computer graphics
3D Point Cloud
A collection of data points in a three-dimensional coordinate system, representing the external surface of an object
Custom
A tailored data annotation tool designed to cater to specific annotation needs not covered by standard tools
