video1.0<iframe src="https://www.loom.com/embed/1433246343c044fcac106cf4138073b6" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/1433246343c044fcac106cf4138073b6-23d02a19dcccba7f.gif677.298In this video, I present my intelligent face tracker project developed for the Catamaran Hackathon, which automatically registers and tracks unique visitors using Python, YOLOv8, InsightFace, and SQL. The system effectively processes video frames, recognizing faces and maintaining accurate counts, achieving 36 unique visitors across 298 frames with no missed or duplicated events. I also address a bug related to tracking continuity that improved accuracy significantly. I encourage you to review the session summary and logs for detailed insights into the performance and functionality of the application.