Objective
To develop an AI-powered system that can automatically detect, track, and analyze advertisements in NBA game videos, providing accurate insights on ad exposure, frequency, and effectiveness for advertisers, broadcasters, and sports organizations.

Description
This project leverages computer vision and deep learning techniques to monitor advertisements displayed during NBA games, including those on digital billboards, courtside banners, and other in-game surfaces. Using models such as YOLO or Faster R-CNN for ad detection, along with OCR to identify brand names and ad content, the system will classify and track ads based on location, brand, and visibility duration. Video processing tools like OpenCV and FFmpeg will be used for frame extraction and pre-processing, while data analytics will provide detailed reports on ad occurrences, exposure time, and effectiveness. The solution also supports real-time ad monitoring, enabling stakeholders to assess sponsorship value, optimize marketing strategies, and maximize revenue potential.