video-search-analysis

To search a long surveillance video for a specific object or scene, use multimodal semantic retrieval to detect and index targets, returning structured segment matches with time ranges and confidence scores.

Yes, you can use natural language retrieval to find video segments of an incident. Multimodal analysis interprets descriptions like "person wearing red clothes" to locate matching moments without manual scrubbing.

Vectorized semantic matching for video search converts video content into high-dimensional vectors, enabling fused fuzzy matching across text, image, and video modalities for multi-condition segment localization.

Video segment localization supports input via both local file uploads and public URLs, processing the sources through cloud APIs to return structured time ranges and optional historical reports.

The best way to retrieve specific moments from long video assets is target-based and vectorized semantic matching, which indexes conventional objects and people to return precise time ranges and reduce wasted viewing time.

You need the requests library installed to use multimodal video search, as the skill relies on this dependency to communicate with cloud APIs for processing and returning segment localization reports.