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Basketball Video Analytics and Player Tracking System

Summary :

We developed an AI-based basketball analytics platform that tracks players, the ball, and in-game events directly from match footage , without the need for sensors or wearables.
This system provides coaches, analysts, and teams with deeper insights into player movements, performance, and strategy through fully automated video analysis.

Problem

Problem

Objectives

  • Build a sensor-free analytics platform that works directly from match footage.
  • Enable real-time player and ball tracking through computer vision.
  • Detect key match events like goals, rebounds, and assists automatically.
  • Provide visual analytics such as heatmaps and positional data for performance analysis.

Challenges

  • Achieving accurate player detection and identification under varying camera angles and lighting.
  • Differentiating between players and referees during rapid in-game motion.
  • Maintaining real-time performance while processing high-resolution match videos.
  • Ensuring tracking stability despite occlusion and overlapping players.

Solution

We built a video analytics system that uses advanced computer vision models to detect, track, and analyze players frame by frame.
The system accurately identifies players, ball trajectories, and event outcomes ,  such as rebounds, passes, and goals ,  directly from standard match recordings.
It generates visual overlays like heatmaps, positional movement trails, and player comparisons, helping teams analyze performance and optimize strategy effortlessly.

Key capabilities include:

  • Player & ball detection using YOLOv8 and custom event classification.
  • Automated tracking and positional mapping in real time.
  • Action detection for offensive and defensive events.
  • Automated match reports with team and player summaries.

Architecture

Match Footage:
The system processes recorded or live basketball game footage as input.
Frame Extraction:
Each frame is separated for analysis to capture player and ball movements precisely.
Object Detection:
AI models identify players, referees, and the ball in every frame using YOLOv8.
Tracking & Event Recognition:
DeepSORT and custom classifiers follow players across frames and detect events like goals, rebounds, or passes.
Analytics Dashboard:
The processed data is visualized as heatmaps, performance stats, and positional summaries for coaches and analysts.

Results & Impact

Sensor-Free Tracking player and ball movement tracking without the need for hardware sensors.

Time Savings for Coaches automates match analysis that previously took hours, freeing staff for strategic preparation.

Data-Driven Coaching Decisions Positional maps, heatmaps, and performance stats to guide training and game-day decisions.

Scalable Across Sports: Quick adaptation to football, hockey, or other team-based sports.

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