Data Annotation Services For Sports Vision

Labellerr Using state-of-the-art AI, improves sports through accurate ball tracking, player security, and object recognition unique to each game. Join us in transforming sports analytics.

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Maximize Computer Vision Security & Surveillance with Labellerr
Why us

Unparalleled Expertise in Computer Vision for Sports

With unmatched experience with computer vision for sports applications. Our state-of-the-art annotation services guarantee the highest level of accuracy for the study of sporting performance. Laborerr's tool and service enables automobile manufacturers minimise costs without sacrificing the highest calibre of automated annotation.

OUr usecase

Production usecase examples

Safety compliance

Cricket/Tennis Ball Tracking

Accurate and timely analysis of ball trajectory in real-time for cricket and tennis players.

Face detection

Helmet Detection

Safeguarding public health by precisely identifying helmets.

Vehicle Surveillance

Cricket-Football-Baseball Classification

Identifying various athletic objects with ease for improved analysis.

Perimeter sensing

Shuttlecock Position Detection

Exceptionally accurate pinpointing of shuttlecock movement.

City management

Boxpunch Detector

Accurately and precisely identifying boxing moves.

Anomaly detection

Cyclist Position Detection

Tracking cyclist positions for performance analysis and safety precautions is known as cycling position detection.


What is Labellerr's expertise in computer vision for sports applications?


As a pioneer in computer vision for sports applications, Labellerr provides accurate annotations for photos and videos in a range of sporting contexts.

The platform's expertise in sports data annotation enables the creation of extremely precise machine learning models. Labellerr is a key player in the advancement of sports analytics and safety compliance with its expertise in athlete tracking, object detection, and event recognition.

How does Labellerr contribute to safety compliance in sports?


With a focus on player safety and equipment monitoring, Labellerr accurately annotates sports-related data to help ensure safety compliance in the industry.

Labellerr makes it easier to construct machine learning models that can track player movements, identify safety dangers, and improve overall safety measures in a variety of sports environments by providing accurate annotations in photos and videos.

Can you provide examples of Labellerr's production use cases in sports?


Labellerr has demonstrated its efficacy in a number of sports production use cases, such as event recognition for improved sports broadcasting, equipment detection for safety compliance, and real-time athlete tracking for performance analysis.

Labellerr is essential to the development of machine learning models that drive innovation in sports analytics and improve the viewing experience overall since it provides precise annotations.

How does Labellerr contribute to sports analytics through AI and computer vision?


Labellerr's use of AI and computer vision has made a significant contribution to sports analytics. Labellerr makes it easier to train machine learning models for tasks like player tracking, action detection, and performance analysis by offering accurate annotations for sports-related data.

These models improve the capabilities of sports analytics and provide insightful information on team tactics, player behaviour, and game dynamics.

What role does Labellerr play in cyclist position detection and anomaly detection in sports?


Using its computer vision capabilities, Labellerr is essential to sports cyclist position tracking and anomaly detection. Labellerr makes it easier to construct machine learning models that can detect and analyse rider positions in real time by precisely annotating photos and videos of cycling events.

This feature is crucial for sports analytics since it allows for precise performance evaluation and early anomaly detection during bicycle competitions.

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