AI video analytics for Forensic Video Search
Forensic video search examines large video archives using detected objects, events, metadata and natural language. It narrows hours of recordings down to relevant sequences for targeted review.
Direct answer
What is forensic video search?
AI-supported forensic video search makes recorded video footage specifically searchable. Instead of viewing recordings completely manually, search criteria such as object class, vehicle features, camera, period or event type can be combined with each other.Depending on the platform, similar image examples or naturally formulated search queries can be used. In this way, relevant sequences become visible more quickly and can be verified in a targeted manner.
Forensic Video Search
The analysis uses recognised features, events and metadata to specifically narrow large video collections.
- ResultThe system provides suitable video sequences with camera, time and relevant search features.
- ResultsForensic video analytics and search in recorded material
- PlatformsSynaEYE, Vaidio
- QualityWe validate recognition, indexing and search results with real positive, negative and borderline cases under the actual camera conditions.
Scope of detection
Forensic Video Search: capabilities and results.
The forensic search combines different analysis features. Which filters are available depends on the models, cameras and the respective platform used.
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Object and event filters
video footage can be narrowed by detecting object classes or events. For example, it is possible to search specifically for people, vehicles, certain object classes or previously recognised safety events.
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Period, Location and Camera
Time, location and individual cameras can be directly included in the search. This means that, for example, research can be limited to a specific camera area between 18:00 and 20:00.
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Vehicle attributes and licence plates
With existing vehicle analysis, additional attributes such as vehicle class, brand, model, colour or licence plate may be used for searching. For example, you can search for a black SUV from a particular brand or a known label.
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Similar picture examples
An existing image or a relevant video hit can serve as a visual reference. The system then searches for recordings with similar visible features and makes possible candidates available for review.
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Text-based and natural search
Depending on the available VLM or Natural Language function, a scene can be described linguistically, for example: “Show people with a red jacket in the entrance area”, “Find vehicles that have driven into the loading zone after 10 pm” or “Show sequences with a person who leaves an object behind”. The system translates the description into a visual search and provides suitable candidates.
From image to process
Forensic Video Search in operational workflows.
The platform combines analysis metadata with the recorded video footage and thereby reduces the manual search effort.
Defining search space
We limit the period, cameras, location and other relevant criteria.
Combining features
Object classes, events, vehicle attributes, or other available analysis characteristics reduce the number of possible hits.
Examining candidates
The system provides suitable sequences with camera, time and visual evidence. Authorised people can check the results immediately in the original video context.
Continue using hits
Relevant hits can be incorporated into existing forensics, security or documentation processes.
Typical applications
Where is forensic video search used?
Forensic video search is suitable for situations in which relevant events have to be found and traced retrospectively within large video collections.
Investigating security incidents
After burglary, theft, vandalism, violence or other security events, the system can search the relevant period based on object, vehicle or event characteristics.
Vehicle Search and Routes
Licence plates, vehicle make, model, colour, and other attributes can be used to find relevant vehicle observations more quickly. In combination with cross-camera tracking, possible vehicle routes can be reconstructed from this.
Personal observations in the investigation
Visual features, cameras and time periods can help to find relevant personal observations more quickly within a defined study case. Depending on the permitted function, additional persistent tracking, facial grouping or controlled facial recognition can be used.
Clarifying processes and operations
In production, logistics or other operational processes, the search can help to clarify subsequently when a particular object, vehicle or process event was visible.
Research via multiple cameras
A single hit can be the starting point for further searching across multiple cameras or time ranges. This allows movement sequences to be reconstructed much faster than by manually viewing each individual image.
Suitable platforms
Which platform supports forensic video search?
SynaEYE and Vaidio combine recorded video with analytics, events and metadata. The suitable range of searches depends on existing models, cameras, storage and the required forensics functions.
Operational video analytics
Continuous AI video analytics for live camera streams
SynaEYE continuously analyses existing cameras in real time and combines detection, event logic, alerts, tracking and forensics in a European edge AI platform.
The right choice if you:want to monitor security, safety or operational processes continuously with your cameras and process detected events immediately.
Enterprise platform
Enterprise Visual AI with maximum functional breadth
Vaidio is an established enterprise platform with an extensive catalogue of standardised video analytics, powerful forensic search and a broad integration ecosystem.
The right choice if you:need a large, standardised enterprise environment with a broad analytics portfolio, multi-site operation and established VMS integrations.
Responsibility and feasibility
Forensic Video Search: limitations and requirements.
Forum video search is only as good as the underlying camera images, analysis models and available metadata. It provides relevant candidates and reduces the search space, but does not automatically replace the check of the original video footage.
Dependence on detection and indexing
If an object or event has not been recognised due to poor image quality, occlusion or model boundaries, it may also be missing in a feature-based search.No hit is no proof against
A search query describes criteria that the system applies to existing material. A missing hit therefore does not automatically prove that the searched event did not take place.Verify similar candidates
Image similarity, person or vehicle attributes may provide incorrect candidates. Such hits must be verified on the basis of the original sequence, especially in safety-critical investigations.Language-based hits as search help
Naturally formulated searches can describe complex scenes, but do not provide guaranteed complete or error-free results. VLM or language-based hits should therefore be treated as a search aid and not as a final determination.Retention periods limit the search space
The search can only access video footage and analysis data that is still available within the defined retention periods.Assess data protection requirements for your specific use case. For EU locations, refer to the GDPR and the EDPB guidelines on video surveillance. Also consider the requirements of the EU AI Act, where applicable to your use case.
For private operators in Switzerland, the FDPIC guidance on video surveillance guidance.
Frequently asked questions
Forensic Video Search explained in brief.
What is forensic video search?
AI-supported forensic video search makes recorded video footage specifically searchable. Instead of viewing recordings completely manually, search criteria such as object class, vehicle features, camera, period or event type can be combined with each other.Depending on the platform, similar image examples or naturally formulated search queries can be used. In this way, relevant sequences become visible more quickly and can be verified in a targeted manner.
What does forensic video search detect?
Configurable inspection criteria include: object and event filters, period, location and camera, vehicle attributes and indicators, similar image examples, text-based and natural search. We validate which of these work reliably using real scenes.
Where is forensic video search used?
Typical applications include testing safety incidents, vehicle search and routes, personal observations in the investigation case, process and operational processes are to clarify searches across several cameras. A defined follow-up process is decisive.
Which platform supports forensic video search?
Suitable platforms include SynaEYE, Vaidio. The final choice depends on the operating model, infrastructure, integrations and data retention.
What are the limitations of forensic video search?
If an object or event has not been recognised due to poor image quality, occlusion or model boundaries, it may also be missing in a feature-based search. A search query describes criteria that the system applies to existing material. A missing hit therefore does not automatically prove that the searched event did not happen. Image similarity, personal or vehicle attributes can deliver wrong candidates. Such hits must be verified, especially in safety-critical investigations, based on the original sequence. Naturally formulated searches can describe complex scenes, but do not provide guaranteed complete or error-free results. VLM or language-based hits should therefore be treated as a search aid and not as a final determination. The search can only access video footage and analysis data that is still available within the defined retention periods.
Does the analytics work with existing cameras?
In many cases, yes. The decisive factors are perspective, resolution, lighting, frame rate, compression and the visible size of the relevant feature.
Validate before scaling
Test forensic video search with your cameras.
We assess the scene, image quality, data protection, integrations and response process using real data.
Discuss your use case
