A Novel Approach to Semantic Search for Home Security Footage Using Generative AI and LLMs

Author(s)	Sibin Thomas
Country	USA
Abstract	This paper looks at how generative AI and large language models (LLMs) could change the way image and video search works in home security systems [1]. We look at the problems with current methods, like manual tagging and simple machine learning-generated metadata, and suggest a new way to use deep learning models to create rich embeddings that show what visual material means semantically [2]. These embeddings, which are kept in vector databases, make natural language-based search easier, so users can ask complicated questions in a conversational way. We talk about how to train and fine-tune LLMs, using methods such as RLHF, context optimization, and RAG, to make searches more accurate and faster [3]. We also look into how to use trends of user access to do proactive indexing and caching of relevant results. This study opens the way for smarter and easier-to-use search tools in home security, making it easier for users to get useful information from their video data.
Keywords	Home security cameras, Image and video search generative AI, LLMs, Deep learning Embeddings, Vector databases, Natural language processing (NLP)
Field	Computer Applications
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-02-22
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.20369
Short DOI	https://doi.org/g8t626

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