You will get ai sports video analysis with player and ball tracking
Project details
Want to turn your game footage into data? I build AI sports video analysis that tracks players and the ball, recognizes actions, and turns raw video into usable stats, for any sport.
What I can analyze:
Player detection & tracking — positions, movement, heatmaps
Ball tracking & trajectory — even fast, tiny balls
Action recognition — passes, shots, strikes, events
Pose & technique analysis
Possession, counts & zone stats
Annotated video + data export (CSV/JSON)
Works for: soccer, basketball, tennis, golf, combat sports, and more.
What you get:
Annotated video with tracking overlays
Stats report / data export
Clean source code + run instructions
Optional live dashboard + API
Why me: I've built real sports CV systems, fast golf-ball tracking and multi-label MMA action recognition, plus production detection running on live cameras. You get working analysis, not a demo, with honest accuracy.
Stack: YOLOv8/v11, PyTorch, OpenCV, pose estimation, Docker.
Send a short clip and tell me what you want measured, I'll show you exactly what's possible before you order.
What I can analyze:
Player detection & tracking — positions, movement, heatmaps
Ball tracking & trajectory — even fast, tiny balls
Action recognition — passes, shots, strikes, events
Pose & technique analysis
Possession, counts & zone stats
Annotated video + data export (CSV/JSON)
Works for: soccer, basketball, tennis, golf, combat sports, and more.
What you get:
Annotated video with tracking overlays
Stats report / data export
Clean source code + run instructions
Optional live dashboard + API
Why me: I've built real sports CV systems, fast golf-ball tracking and multi-label MMA action recognition, plus production detection running on live cameras. You get working analysis, not a demo, with honest accuracy.
Stack: YOLOv8/v11, PyTorch, OpenCV, pose estimation, Docker.
Send a short clip and tell me what you want measured, I'll show you exactly what's possible before you order.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, BigDL, Google AutoML, Keras, MLflow, NVIDIA AI Platform, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$120
|
Standard
$450
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 0 |
AI Model Integration | - | ||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | - | |
Ontology | |||
Source Code | |||
Taxonomy | - |
Frequently asked questions
About Muhammad Muneeb
Senior Computer Vision Engineer | Real-Time AI Video Analytics
Rawalpindi, Pakistan - 12:37 am local time
I build real-time AI systems that turn live video into actionable intelligence, whether that’s detecting fire in a facility, flagging suspicious behavior in retail, or analyzing player movement in sports.
Most computer vision models work in a notebook.
Mine run in production, on edge devices, in the cloud, or across multi-camera systems.
I specialize in designing full end-to-end pipelines:
• Data strategy and annotation design
• Model selection, training and fine-tuning
• Latency optimization (real-time performance)
• Multi-object tracking and action recognition
• Edge deployment (Jetson, TensorRT)
• Cloud APIs and scalable architecture
• Monitoring and performance improvement
🔎 What I Build
🚨 Surveillance & Safety AI
Real-time fire detection, shoplifting detection, intrusion monitoring, license plate recognition, behavioral analysis systems.
⚽ Sports Analytics
Player tracking, action recognition, pose-based performance analysis, automated highlight tagging, multi-camera tracking pipelines.
🏭 Industrial Vision
Defect detection, visual inspection systems, OCR pipelines, automated monitoring.
⚙ Technical Foundation
Deep Learning: PyTorch, TensorFlow
Detection & Tracking: YOLO, DeepSORT, ByteTrack
Action Recognition: SlowFast, R(2+1)D
Optimization: TensorRT, ONNX, Edge deployment
Backend & APIs: FastAPI, Flask
Cloud: AWS, GCP
Deployment: Docker, GPU inference pipelines
I don’t just train models.
I design production-ready AI systems that remain accurate under poor lighting, occlusion, motion blur, and real-world constraints.
If you are building:
• A smart surveillance system
• A sports analytics product
• A real-time AI camera application
• An edge-deployed vision system
Send me a brief description of your use case, and I’ll outline how I would architect your solution.
Let’s build something that works outside the lab.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Muneeb can start the project.
Delivery time starts when Muhammad Muneeb receives requirements from you.
Muhammad Muneeb works on your project following the steps below.
Revisions may occur after the delivery date.
Detection & tracking setup
I tune object detection and multi-object tracking to your sport and camera angle, so every player and the ball is followed reliably across the full video.
Analysis & stats extraction
I extract the metrics you need — player movement, positions, heatmaps, possession, counts, and action/event tagging — and compile them into clean stats.