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Basketball Shot Detection

Sports Analytics & Computer Vision

Basketball Shot Detection

ACCURACY

~85%

Real-World Footage

RELIABILITY

+15%

With Custom Filter

DETECTION

YOLOv8

Ball, Hoop & Players

SHOT TYPES

3+

Layup, Jump, Three-Ptr

SEGMENTATION

SegFormer

Court Segmentation

Background

When elite performance is capped by manual observation

Human analysts cannot track every movement across a fast-paced court without error. This reliance on manual review causes critical tactical insights to be lost to fatigue and subjective interpretation.

The Intellema Design Challenge

Basketball performance analysis typically requires labor-intensive manual annotation, which is prone to inconsistencies and delays. This slow process makes it hard for athletes to get the immediate feedback they need to improve their skills.

This project addressed the need for an automated system capable of identifying shots, tracking players, and generating real-time analytics from raw video. The system provides coaches with objective, scalable data to refine training and strategy.

  • Manual Annotation
  • Data Bottlenecks
  • Analysis Errors
  • Motion Tracking

Our Approach

Tech Stack

Python
PyTorch
Hugging Face
TensorFlow
OpenCV

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