Tag: Video analysis

In this post, we demonstrate how to use large vision models (LVMs) for semantic video search using natural language and image queries. We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on Amazon SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. Finally, we use Amazon OpenSearch Serverless with its vector engine for low-latency semantic video search.

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime) to a centralized video insights and summarization engine. This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. The solution notes the logged actions per individual and provides suggested actions for the uploader. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.