Semantic Search Over Dashcam Footage Using Gemini Embedding 2
SentrySearch is an open-source Python CLI that enables semantic search over dashcam footage using Google's Gemini Embedding 2 model, which maps text, images, audio, and video into a single vector space.
Why it matters
SentrySearch demonstrates how advancements in multimodal AI models like Gemini Embedding 2 can enable powerful semantic search capabilities over unstructured video data, improving productivity and saving time for users.
Key Points
- 1SentrySearch allows users to search raw video files in plain English and get back trimmed clips of the matching moments
- 2The tool uses Gemini Embedding 2, a multimodal embedding model that can directly compare text queries to video clips
- 3The pipeline involves chunking videos, embedding the chunks, storing the vectors, and performing cosine similarity search on text queries
Details
SentrySearch is a command-line tool that brings semantic search capabilities to dashcam or video libraries. It works by first indexing the footage, splitting each video into 30-second chunks and embedding them using Google's Gemini Embedding 2 model. This model can map text, images, audio, and video into a single unified vector space, allowing direct comparison between a text query and the video content. During the search process, the user's text query is also embedded and matched against the video chunks using cosine similarity to find the most relevant clips, which are then automatically trimmed and returned. This eliminates the need for intermediate steps like transcription, frame captioning, or optical character recognition, making the search process fast and efficient.
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