Security teams can accumulate enormous amounts of recorded video across cameras, buildings, and locations. When an incident occurs, finding the few minutes that matter within months of footage has traditionally required analysts to manually review recordings and track subjects across multiple cameras. AI-driven natural language search is changing that process by making recorded video searchable in a much more intuitive way.
1: Search Security Video Using Everyday Language
Large vision models can connect written descriptions with visual information contained in video. Semantic video search allows users to retrieve relevant video content using natural-language text queries or descriptions rather than relying only on traditional search parameters [1].
For a security team, that could mean entering a request such as, “Show a person in a red jacket carrying a backpack near the loading dock last Tuesday.” Instead of manually reviewing hours of recordings camera by camera, the system can search indexed video information for segments that conceptually match the request.
Large vision models can create semantic representations of individual video frames, which can then be stored in a searchable vector index for efficient retrieval and similarity searches [1]. Search results can also be grouped by timestamps to return coherent video segments instead of isolated frames.
2: Turn Days of Manual Review Into Faster Investigations
The biggest advantage is speed. A task that might require junior security analysts to spend days manually tracking a person, vehicle, object, or event across recorded footage can potentially be compiled into an incident timeline in less than 60 seconds with the right AI-driven system.
This faster retrieval can help security teams reconstruct events sooner, investigate losses, and begin asset recovery efforts without spending valuable time manually searching every recording. Multimodal search systems can convert a user’s plain-language query into an embedding and compare it with indexed visual data to identify similar images or videos [2].
The technology is not infallible. Video quality and resolution can affect search performance, and blurred or low-resolution footage can make it more difficult for large vision models to identify objects and fine visual details [1]. AI search should therefore accelerate the investigative process while allowing security professionals to review and validate the resulting footage.
3: Metadata and Retention Still Matter
Fast search is only useful when the necessary information is still available. Security teams need to understand how long their system retains lightweight metadata or index information used for searching compared with the underlying high-resolution footage.
Video retention requirements affect the amount of storage an organization needs, while factors such as frame rate, resolution, and compression can cause stored video to consume significant amounts of data [3]. Storing footage only at a very low bit rate can also permanently eliminate visual information that may later be needed for analysis.
Metadata deserves similar attention. Converting or exporting surveillance video can result in lost metadata and degraded image quality, while standardized date and time metadata can improve the usefulness of CCTV video for subsequent analysis [4].
4: Preserve Chain of Custody During AI-Assisted Searches
AI does not eliminate the need for sound evidence-handling procedures. Chain of custody tracks the movement and control of an asset by documenting who handled it, when it was collected or transferred, and the purpose of the transfer [5]. A break in that process can call the integrity and reliability of digital records into question.
Forensic video practices also emphasize preserving original evidence. Video processing should be performed on a working copy and documented so the methods used can be reproduced and independently evaluated [6]. Security teams should make sure AI-assisted search, retrieval, export, and storage processes align with their corporate evidentiary standards and retention policies.
Make Recorded Security Video More Actionable With Surveillance Secure
AI-driven natural language search can transform recorded security video from a massive archive into a resource investigators can search using the same language they use to describe an incident. When paired with appropriate retention, metadata, and chain-of-custody practices, that speed can help organizations respond to incidents and pursue asset recovery more efficiently.
Contact Surveillance Secure today to explore how advanced video surveillance and AI-driven search capabilities can help your security team investigate incidents faster and get more value from recorded video.
Sources:
- https://aws.amazon.com/blogs/machine-learning/implement-semantic-video-search-using-open-source-large-vision-models-on-amazon-sagemaker-and-amazon-opensearch-serverless/
- https://cloud.google.com/blog/products/data-analytics/multimodel-search-using-nlp-bigquery-and-embeddings
- https://www.nist.gov/ctl/pscr/vqips-storage
- https://www.nist.gov/programs-projects/digital-video-exchange-standards
- https://www.cisa.gov/sites/default/files/2023-12/Chain%20of%20Custody_2023.8.14_508.pdf
- https://www.swgde.org/documents/published-complete-listing/18-v-001-best-practices-for-digital-forensic-video-analysis/
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