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AI Video Intelligence for CCTV with Object Detection, Licence-Plate Recognition and Natural-Language Search
Case Study Topics
Client Overview
The end client is a security and surveillance technology initiative focused on helping authorised operators and investigators extract actionable intelligence from recorded CCTV footage.
The organisation required a proof-of-concept platform capable of automatically analysing uploaded videos and making large volumes of footage searchable by object, licence plate, and natural-language description.
The end client’s identity is withheld due to contractual confidentiality and NDA obligations.
Project Overview
The project involved the development of a proof-of-concept video-intelligence platform for analysing recorded CCTV footage.
The solution was designed and developed by a SilwaTech partner company through SilwaTech’s partner delivery ecosystem.
The platform combined three core AI capabilities
- Object detection and multi-object tracking
- Licence-plate localisation and recognition
- Semantic search across AI-captioned video frames
Operators could upload MP4 footage, select the required analysis modules, and search the processed results using object classes, licence-plate details, or natural-language descriptions.
The proof of concept was designed to demonstrate how multiple computer vision and generative AI models could operate through one investigation workspace while preserving human review of uncertain results.
Business Challenge
- Traditional surveillance investigations depended heavily on operators manually reviewing large volumes of recorded footage.Key challenges included
- Hours of manual review to locate a specific vehicle, object, or event
- No ability to search footage by scene meaning or plain-language description
- Separate tools for object detection, tracking and licence-plate recognition
- Difficulty identifying the precise frame in which an event occurred
- Limited visibility of movement and activity across a video timeline
- Re-uploading footage whenever additional analysis was required
- Need to distinguish high-confidence results from detections requiring human review
- Difficulty exporting structured findings for further investigation
The client needed a unified multimodal platform that could process footage once, apply multiple AI pipelines, and return frame-level results through a searchable investigation workspace.
Solution Architecture
Solution Delivered
Object Detection and Tracking
A computer vision pipeline was implemented to identify and track relevant objects across uploaded footage.
Key capabilities included
- Detection across 80 object classes
- Classification of detected objects
- Confidence scoring for each detection
- Bounding-box and visual-overlay generation
- Persistent tracking identifiers across frames
- Frame sampling at one frame per second
- Optimised image width for efficient processing
- Batched processing and storage of detection results
Licence-Plate Recognition
A dedicated recognition pipeline was implemented to support vehicle identification.
The solution provided
- Licence-plate localisation within vehicle detections
- Dual-pass OCR across cropped vehicle and plate images
- Extraction of licence-plate characters
- Validation against supported regional plate formats
- Confidence scoring of recognised plates
- Manual-review flags for uncertain results
- Search by complete or partial licence-plate information
Semantic Search and Investigation Workspace
Semantic AI capabilities allowed operators to search video footage using plain-language descriptions.
The investigation workspace included
- AI-generated captions for selected video frames
- Vector embeddings representing visual scenes
- Combined search across objects, plates and semantic descriptions
- Adjustable search sensitivity
- Global and individual-video search options
- Deep links to the relevant video position
- Toggleable, colour-coded visual overlays
- Activity-density timeline divided into 72 navigable intervals
- Click-to-seek navigation
- Dedicated views for objects, plates and semantic insights
- One-click CSV export of investigation results
Video Processing Pipeline
A scalable and non-destructive processing workflow was implemented to manage uploaded footage and derived intelligence.
Capabilities included
- Chunked streaming of large video files
- Cloud object storage
- CDN-supported video delivery
- Background AI processing
- Live processing-progress updates
- Independent activation of analysis modules
- Reprocessing of existing footage without uploading it again
- Preservation of the original video alongside derived results
Technology Stack
The technology environment included
- YOLOv8 and RT-DETR for object detection
- ByteTrack for multi-object tracking
- EasyOCR for licence-plate character recognition
- SigLIP for visual embeddings
- BLIP for frame-caption generation
- PostgreSQL and pgvector for metadata and vector search
- FastAPI for backend processing and search services
- Next.js for the operator interface
- Cloud object storage for video files
- CDN services for video delivery and playback
- Background processing services for computer vision pipelines
Engagement Model
The project was delivered through SilwaTech’s partner ecosystem under a partner-led proof-of-concept model.
The technical architecture, AI model integration, backend development, user-interface development, testing and proof-of-concept implementation were performed by an experienced SilwaTech partner company. SilwaTech contributed through engagement alignment, partner collaboration and coordination of the required computer vision and video-intelligence capabilities.
The delivery team included specialists in
- Computer vision engineering
- Object detection and tracking
- OCR and licence-plate recognition
- Multimodal embeddings and semantic search
- Backend and data engineering
- Frontend application development
- Cloud video processing
- Quality assurance and security testing
The engagement progressed through staged requirements analysis, model evaluation, pipeline development and operator-workspace prototyping. The architecture also provided a defined path towards larger-scale processing and integration with existing surveillance infrastructure.
The identities of the end client and implementation partner remain confidential. SilwaTech does not represent itself as the direct contracting party or sole technical implementer for this engagement.
Security and Governance Focus
Surveillance footage, vehicle information and investigation results required controlled access and careful handling.
Key areas of focus included
- Authenticated access for authorised operators
- Role-based access to footage, detections and exports
- Encryption of video and derived data in transit and at rest
- Preservation of original uploaded footage
- Non-destructive processing of video evidence
- Traceable AI-generated results
- Confidence scoring for detections and recognised plates
- Human review of low-confidence findings before evidential use
- Controlled export of investigation data
- Data-handling practices designed to support applicable privacy requirements
These controls supported the responsible and reviewable use of AI-generated surveillance intelligence.
Value Delivered
The proof of concept demonstrated
- Automated tagging across 80 object classes
- Persistent tracking of detected objects across video frames
- Integrated licence-plate recognition
- Natural-language search across recorded footage
- Frame-level investigation results
- Faster navigation to relevant events
- Unified object, plate and semantic analysis
- Visual overlays supporting operator verification
- Reprocessing of existing footage without repeated uploads
- Structured export of investigation findings
- Reduced dependence on manual footage review
- A scalable foundation for future surveillance-system integration
Why Silwatech
This engagement demonstrates the type of advanced video-intelligence capability SilwaTech can support through its technology partner ecosystem, including
Access to specialists in object detection, tracking and OCR
Experience with multimodal AI and vector-search architectures
Integration of multiple vision models into unified workflows
Human-review controls for uncertain AI outputs
Secure handling of sensitive video and derived metadata
Structured progression from proof of concept to integrated deployment
Collaboration with specialised computer vision implementation partners
Selected Enterprise & Public Sector Experience
Our project experience spans government authorities, regulated enterprises, and major organisations across multiple industries.
References shown may include direct SilwaTech engagements and work delivered through our contracted partner network. Inclusion does not imply a direct contractual relationship with, or endorsement of, SilwaTech.
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