VIDEO MOMENT RETRIEVAL SYSTEM

Abstract

The Video Moment Retrieval System presents an innovative solution to address the growing demand for efficient video content search and retrieval, utilizing advanced techniques in natural language processing (NLP), deep learning, and computer vision to bridge the semantic gap between textual descriptions and video content. By employing pre-trained models, such as transformers for text encoding and convolutional neural networks (CNNs) for video frame analysis, the system indexes video content, associating each segment with relevant keywords, actions, or contexts. Users can submit text-based queries like “Show me the moment when the character A reveals the secret,” and the system analyzes both temporal and spatial features within the video to identify corresponding
moments. The system’s primary applications include educational platforms, entertainment, surveillance, and content moderation, where quick access to specific moments is essential. For example, students can search for specific lessons or moments in video lectures, entertainment users can pinpoint favorite scenes, security personnel can quickly find incidents in surveillance footage, and content moderators can efficiently flag inappropriate material. By providing accurate, time-saving search capabilities, the Video Moment Retrieval System reduces manual search efforts, enhances user experience, and improves overall productivity across sectors by enabling fast and precise retrieval of video moments.