A Comprehensive Framework for Frame Detection Leveraging SIFT and Visual Feature Characterization

This project focuses on developing a system that can identify videos using individual frames or short sequences. This is a complex task, but it has the potential to revolutionize how we interact with video content in many industries, from entertainment to security. The ability to identify videos from just a still frame or short video segment is a complex yet highly demanded task in industries ranging from entertainment to security. The system will use visual feature extraction and a comprehensive database to match frames to videos. The methodology involves using a combination of SIFT, YOLOv5, and ResNet-50 to process and analyze the frames. ChromaDB, a vector database for AI applications, is used to store and search for matches. The system will then use a modified ensemble ranking system that considers factors like frequency, consistency, and tag coverage to calculate a confidence score for each match. This score will be displayed to the user along with the matched videos. The project aims to provide a user-friendly interface that allows users to upload images and view the predicted videos, as well as the calculations performed during the matching process. Future improvements include refining the algorithm for finding unique frames, enhancing the user interface with history tracking, and improving the confidence calculation algorithm.