Advanced On-Premise NVR System Development
Budget: $1,500 – $3,000 USD
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I am looking for an Enterprise-Grade NVR System with LPR, Face Recognition, and Optional Vehicle Classification (Frigate+, YOLO, or Alternative)
We are building a secure, on-premise NVR system for enterprise use. Our top priorities are:
License Plate Recognition (LPR)
Face Detection and/or Recognition
Real-time or near real-time performance
On-premise processing only (no cloud dependencies)
Scalable across multiple cameras and locations
Efficient resource usage (Coral TPU, GPU, or similar)
Optional capabilities we’re interested in:
General vehicle type detection (e.g. sedan, SUV, truck)
Fine-grained vehicle identification, specifically distinguishing between models such as Toyota Hilux vs Nissan Navara
Vehicle color detection
We are currently considering Frigate+ with YOLO, but are open to better or more efficient alternatives that can meet these goals.
It would need an NVR interface for live and playback, but that will depend on the base we use, freight, shinobi, etc
Requirements:
Proven experience building or deploying video analytics systems at scale
Familiarity with detection pipelines and integration of LPR and facial recognition
Ability to trigger OCR or facial match pipelines from detection events
Capable of optimizing system performance for low-latency detection
Must deliver working configurations/scripts and clear documentation
Nice to have:
Experience with Frigate+ and custom YOLO training
Integration with Plate Recognizer, CompreFace, or similar
Setup of alerts, search tools, or dashboards for reviewed footage
To apply:
Describe your relevant experience with similar projects
Outline your recommended tech stack and why
Share links to your work (GitHub, screenshots, videos, or brief case studies)
I am looking for an Enterprise-Grade NVR System with LPR, Face Recognition, and Optional Vehicle Classification (Frigate+, YOLO, or Alternative)
We are building a secure, on-premise NVR system for enterprise use. Our top priorities are:
License Plate Recognition (LPR)
Face Detection and/or Recognition
Real-time or near real-time performance
On-premise processing only (no cloud dependencies)
Scalable across multiple cameras and locations
Efficient resource usage (Coral TPU, GPU, or similar)
Optional capabilities we’re interested in:
General vehicle type detection (e.g. sedan, SUV, truck)
Fine-grained vehicle identification, specifically distinguishing between models such as Toyota Hilux vs Nissan Navara
Vehicle color detection
We are currently considering Frigate+ with YOLO, but are open to better or more efficient alternatives that can meet these goals.
It would need an NVR interface for live and playback, but that will depend on the base we use, freight, shinobi, etc
Requirements:
Proven experience building or deploying video analytics systems at scale
Familiarity with detection pipelines and integration of LPR and facial recognition
Ability to trigger OCR or facial match pipelines from detection events
Capable of optimizing system performance for low-latency detection
Must deliver working configurations/scripts and clear documentation
Nice to have:
Experience with Frigate+ and custom YOLO training
Integration with Plate Recognizer, CompreFace, or similar
Setup of alerts, search tools, or dashboards for reviewed footage
To apply:
Describe your relevant experience with similar projects
Outline your recommended tech stack and why
Share links to your work (GitHub, screenshots, videos, or brief case studies)