You will get Enterprise AI Video Analysis Platform
Project details
The AI Rugby Video Analysis Platform is an enterprise-grade solution designed to automate the extraction of tactical insights from match footage using advanced Computer Vision and Deep Learning. Built as a scalable, distributed system, it serves coaches and analysts by converting raw video into structured telemetry data.
At its core, the platform leverages a sophisticated multi-stage AI pipeline. It utilizes YOLOv8 for high-precision detection of players and the ball, coupled with ByteTrack algorithms to maintain persistent player identities even during heavy occlusion. Furthermore, it employs 3D-CNN architectures to recognize and timestamp complex game events such as Scrums, Rucks, Mauls, and Lineouts, providing a granular breakdown of match flow.
Technically, the architecture is built on an asynchronous, event-driven backbone using FastAPI, Celery, and Redis. This ensures high availability by decoupling video ingestion from heavy GPU processing. Data is managed through a polyglot persistence layer, utilizing PostgreSQL for relational metadata, MongoDB for massive frame-by-frame analytics, and S3 for cost-effective storage of 4K media.
At its core, the platform leverages a sophisticated multi-stage AI pipeline. It utilizes YOLOv8 for high-precision detection of players and the ball, coupled with ByteTrack algorithms to maintain persistent player identities even during heavy occlusion. Furthermore, it employs 3D-CNN architectures to recognize and timestamp complex game events such as Scrums, Rucks, Mauls, and Lineouts, providing a granular breakdown of match flow.
Technically, the architecture is built on an asynchronous, event-driven backbone using FastAPI, Celery, and Redis. This ensures high availability by decoupling video ingestion from heavy GPU processing. Data is managed through a polyglot persistence layer, utilizing PostgreSQL for relational metadata, MongoDB for massive frame-by-frame analytics, and S3 for cost-effective storage of 4K media.
Machine Learning Tools
GPT-3, MLflow, NLTK, NumPy, Python, Python Scikit-Learn, PyTorch, TensorFlow, Word2vecWhat's included
| Service Tiers |
Starter
$2,000
|
Standard
$2,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 20 days | 30 days | 45 days |
Number of Revisions | 0 | 0 | 0 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
About Ramesh
AI Architect (MLOps | Devops | .NET | AWS | Terraform)
79%
Job Success
Chennai, India - 2:37 am local time
Director | CTO | Chief AI Architect
[Email: rameshrmca2001@gmail.com] | [Phone: 9444856420] | [Location: India]
EXECUTIVE PROFILE
Strategic Technology Leader with over 25 years of experience specializing in Enterprise Architecture, Generative AI (LLMs), and MLOps. Proven expertise in architecting high-volume, business-critical systems across Banking, Retail, and Manufacturing. A hands-on leader who has successfully transitioned legacy infrastructures into modern, AI-driven ecosystems using AWS and Azure. Certified in TOGAF, PMP, and CSM, with a career-long commitment to TDD, Agile methodologies, and scalable distributed systems.
TECHNICAL SKILL MATRIX
Leadership: Strategic Planning, Product Modernization, Capex/Opex, Team Mentorship (20+).
AI/ML & Data: Generative AI (LLMs), PyTorch, TensorFlow, MLOps, Feature Stores, Computer Vision.
Cloud & DevOps: AWS, Azure, Docker, Kubernetes, CI/CD for ML, Jenkins, Terraform.
Architecture: TOGAF 9, Microservices, Event-Driven Architecture (EDA), SOA, DDD, TDD.
Software Stack: .NET Core, Java, Python, Node.js, React, Angular, ASP.NET MVC, WCF.
Databases: SQL Server (2017+), Oracle, MongoDB, Sybase, PL/SQL.
Steps for completing your project
After purchasing the project, send requirements so Ramesh can start the project.
Delivery time starts when Ramesh receives requirements from you.
Ramesh works on your project following the steps below.
Revisions may occur after the delivery date.
Phase 1: Environment Setup & Data Preparation
Baseline Dataset Data Augmentation Custom Data Collection
Phase 2: Player and Ball Detection & Tracking
Fine-tune YOLOv8 Integrate DeepSORT Testing and Refinement