Offline Tennis Player Tracking and Video Annotation
Year
2026
Industry
Broadcast sport and journalistic
Client
Media Solutions
Overview
I developed an offline video analysis system to track a single tennis player in recorded match videos. The goal was to create stable visual tracking and structured outputs for post-match analysis and broadcast workflows.
The challenge
In recorded tennis matches, it is difficult to consistently track one specific player throughout the entire video.
Standard multi-object tracking can cause:
Player ID switching
Unstable bounding boxes
Visual jitter
The system needed to:
Track only the far-side player
Keep tracking stable during the full video
Generate usable outputs for analysis and broadcasting
Work offline (not real-time)
My solution
I built an offline processing system that works on pre-recorded match videos.
For each video, a manual rectangular ROI (Region of Interest) is selected to define the far-side court area. Inside this ROI, a person detection model identifies the player.
Instead of using standard multi-object tracking, I implemented a custom identity-locking logic. This logic keeps tracking focused on the same player by applying:
Spatial distance constraints
Vertical position consistency
Bounding box size stability rules
This approach reduces identity switching and keeps the bounding box stable during the entire match.
Each processed frame includes:
Visible ROI area
Player bounding box
Position coordinates
Confidence score
The system generates:
A fully annotated video
A fixed-resolution cropped video showing only the tracked player
These outputs can be used in post-production tools or broadcast software.
The result
The prototype delivers a stable and reliable single-player tracking system.
Main results:
Consistent player tracking across full match
Reduced visual instability
Clean annotated video output
Structured metadata for analysis
Offline batch processing for post-match workflows
The system provides a practical solution for sports video analysis and content production.
System type :
Computer Vision • Video Analysis • Offline Processing
Role :
Prototype Design • Model Integration • Tracking Logic Implementation
Related cases
Clear architecture and opeerational impact. Discover how intelligent systems improve efficiency, visibility and decision-making across industries.
Key features
Offline batch processing (no real-time dependency)
Manual ROI selection per video
Single far-side tennis player detection and tracking
Stable, locked bounding box across the entire video
Confidence scoring per frame
Annotated full-video output
Fixed-resolution cropped player-only video
Local Python-based prototype application
Tags :
Computer Vision,Sports Analytics,Video Processing,Object Detection,Tracking Systems,Offline Analytics
