Leveraging 5G networks for event video summarization via multimodal analysis

Abstract With the rapid growth of video content across domains such as live streaming, remote education, surveillance, and virtual meetings, there is an increasing need for intelligent systems that can automatically analyze and summarize key events from video data. This paper presents an end-to-end framework that leverages 5G connectivity, Automatic Speech Recognition (ASR), Optical Character Recognition (OCR), and Large Language Models (LLMs) to generate concise, context-aware event summaries in the form of video highlights and textual overviews, eliminating the need to watch full videos. The system calibrates energy thresholds for ambient noise, extracts speech, and identifies keyframes for OCR, using HSV-based color histogram differencing. Redundant OCR outputs are filtered using a Levenshtein-based similarity threshold to retain unique content. TextRank is applied to reduce token load before invoking the OpenAI API for abstractive summarization, with per-minute summaries providing high-level insights. These summaries are overlaid by an Augmented Reality (AR) application, powered by a Network Application that receives streaming, performs instance segmentation to identify speakers and employs a decision-making module that determines the placement of the summaries within the user’s field of view. Experimental evaluation on a private 5G testbed demonstrates smooth 4K streaming with low latency, stable Channel Quality Indicator (CQI), and no frame loss, confirming the system’s scalability and effectiveness in dynamic network conditions.