Abstract
A research paper titled "Video Object Search: A Semantic Approach with OpenAI's CLIP Technology" examines the possibilities of semantic search in the identification of video objects. The technique that is proposed in this paper integrates textual and visual data to enhance the precision of video identification of objects. The conventional approach in video object detection utilizes only visual data, which might result in limited or incorrect item identification. By integrating certainty estimation, adaptability to handle a variety of questions, and semantic comprehension, the recommended approach gets over these limitations. The system evaluates textual and visual data using OpenAI's CLIP Technology, enabling more precise identification of objects in videos. The study's findings show that the suggested approach works more precisely than the conventional approach.
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