You will get Computer Vision for Sports Video Analytics | Player Tracking
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Project details
You will get a production-grade sports video analytics pipeline that turns match or training footage into measurable insights: player & ball detection, multi-object tracking, trajectories, speed, heatmaps, and event detection.
The system is built for real-world conditions — occlusions, motion blur, lighting changes — and runs offline or in real time depending on your constraints.
Core stack: YOLO, ByteTrack / DeepSORT, MediaPipe, OpenCV, PyTorch, TensorFlow, TensorRT, Docker.
Depending on the scope, the system can include:
— Pose-based motion analysis and biomechanical metrics
— Camera calibration and field mapping for cross-angle consistency
— Annotated video overlay and event timeline export
— REST API or containerized deployment optimized for latency and throughput
Output formats: JSON/CSV metrics, annotated video, trajectory data, heatmaps, source code, model weights, and full documentation.
Best fit for: athlete analytics platforms, academy performance tools, sports tech startups validating a PoC before scaling to MVP, and broadcast analytics teams.
The system is built for real-world conditions — occlusions, motion blur, lighting changes — and runs offline or in real time depending on your constraints.
Core stack: YOLO, ByteTrack / DeepSORT, MediaPipe, OpenCV, PyTorch, TensorFlow, TensorRT, Docker.
Depending on the scope, the system can include:
— Pose-based motion analysis and biomechanical metrics
— Camera calibration and field mapping for cross-angle consistency
— Annotated video overlay and event timeline export
— REST API or containerized deployment optimized for latency and throughput
Output formats: JSON/CSV metrics, annotated video, trajectory data, heatmaps, source code, model weights, and full documentation.
Best fit for: athlete analytics platforms, academy performance tools, sports tech startups validating a PoC before scaling to MVP, and broadcast analytics teams.
Machine Learning Tools
Amazon SageMaker, Keras, MLflow, NumPy, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, Python, PyTorch, TensorFlowWhat's included
| Service Tiers |
Starter
$12,000
|
Standard
$30,000
|
Advanced
$50,000
|
|---|---|---|---|
| Delivery Time | 14 days | 42 days | 70 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - |
Frequently asked questions
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Samuel F.
May 12, 2026
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Hartley T.
Aug 13, 2025
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Great team to work with! Highly recommend.
XP
Xhensila P.
Dec 10, 2024
60 minute consultation
We booked a consultation with Ievgen to conduct a technical analysis of potential approaches to solving a complex problem.
Ievgen was highly professional, asked insightful questions, and took the time to thoroughly understand the issue. He carried out a detailed analysis of all possible solutions and promptly delivered the requirements and analysis right after the call.
He also made himself available for follow-up questions, ensuring clarity even after the consultation ended. Communication was excellent throughout the process.
We highly recommend him!
Ievgen was highly professional, asked insightful questions, and took the time to thoroughly understand the issue. He carried out a detailed analysis of all possible solutions and promptly delivered the requirements and analysis right after the call.
He also made himself available for follow-up questions, ensuring clarity even after the consultation ended. Communication was excellent throughout the process.
We highly recommend him!
GA
Guto A.
Nov 20, 2024
Senior iOS Engineer - Computer Vision and Machine Learning Specialist
DF
Dean F.
Mar 13, 2024
30 minute consultation
Levgen is absolutely incredible and such a smart individual!
About Ievgen
Computer Vision & AI Architect | Deep Learning, Audio AI, Edge ML
100%
Job Success
Lviv, Ukraine - 3:22 am local time
My work focuses on production AI systems, not research prototypes. I help companies move from early feasibility studies and PoC development to scalable deployments running in real environments.
Typical projects include:
• computer vision pipelines for detection, tracking, and segmentation
• real-time video analytics and edge AI deployments
• 3D vision and spatial perception systems
• speech processing and audio AI solutions
• generative AI pipelines for image, video, and audio
Computer Vision Systems
Design and development of advanced computer vision pipelines for image and video analysis.
Typical solutions include:
• object detection and multi-object tracking
• semantic and instance segmentation
• pose estimation and motion analysis
• OCR and document understanding
• visual search and recognition systems
These systems are used in industries such as manufacturing, sports analytics, retail, healthcare, and security.
3D Vision & Spatial AI
Development of AI systems that understand spatial structure and depth.
Experience includes:
• structure-from-motion (SfM)
• photogrammetry pipelines
• depth estimation models
• NeRF and neural rendering
• point cloud processing and 3D reconstruction
Applications include robotics, AR/VR, construction analytics, and digital twins.
Edge AI & On-Device ML
I specialize in deploying ML models on mobile and embedded devices where latency, memory, and power constraints are critical.
Typical optimization techniques include:
• model quantization and pruning
• architecture optimization
• real-time inference pipelines
• deployment on mobile and embedded hardware
Technologies include:
TensorRT, TensorFlow Lite, CoreML, ONNX Runtime.
Many deployed systems operate with 50–100 ms inference latency depending on hardware.
Generative AI for Vision & Video
Development of generative pipelines for media processing and synthetic data generation.
Typical solutions include:
• image and video generation pipelines
• diffusion-based editing and enhancement
• synthetic dataset generation for model training
These tools help accelerate AI training and improve model robustness.
Audio & Speech AI
Development of AI systems for speech processing, audio analysis, and voice technologies.
Examples include:
• phoneme segmentation and pronunciation analysis
• speech recognition pipelines
• voice feature extraction and audio analytics
• generative audio and music models
These systems are used in:
• language learning platforms
• speech therapy tools
• voice biometrics systems
• music AI applications
Technical Stack
Frameworks & Models
PyTorch, TensorFlow, OpenCV, Detectron2, MediaPipe, YOLO, DINO, SAM, CLIP
Deployment
TensorRT, TensorFlow Lite, CoreML, Docker, ONNX Runtime, FastAPI
Programming
Python, C, C++
3D Vision
NeRF, SLAM, Dust3r, point clouds
Leadership & R&D
I lead an R&D-focused AI team at It-Jim, an AI consulting company with 30+ engineers and 10+ PhDs specializing in:
• Computer Vision
• Generative AI
• Audio & Speech AI
• Edge AI systems
We help companies solve technically challenging AI problems and build reliable production systems.
If you are looking for experienced AI engineers to design, prototype, or deploy advanced machine learning solutions: feel free to reach out!
Steps for completing your project
After purchasing the project, send requirements so Ievgen can start the project.
Delivery time starts when Ievgen receives requirements from you.
Ievgen works on your project following the steps below.
Revisions may occur after the delivery date.
Scope alignment & success metrics
Confirm KPIs, inputs, outputs, real-time constraints, and acceptance criteria. Finalize “what done looks like” and the tier scope.
Data review & baseline plan
Review sample videos, camera setup, and edge cases (occlusion, blur, lighting). Define detection/tracking approach and evaluation metrics.