Turn Every Camera Into a Source of Business Intelligence
Trained video analytics applies computer vision models to your camera infrastructure — transforming security footage into operational intelligence. Count people, detect safety violations, monitor equipment states, identify unauthorized access, and measure dwell times — automatically, at every camera, continuously.
What RLM Delivers on Trained Video Analytics
Most enterprise camera infrastructure captures footage that is only ever reviewed after an incident. Trained video analytics changes that equation — making every camera an active data source that generates operational insights in real time without requiring human monitoring.
How We Approach Trained Video Analytics
A structured path through the Trained Video Analytics decision — current-state discovery, shortlist and benchmark, commercial negotiation, then support until it is actually working.
Use Case Definition & Camera Audit
We identify the specific video analytics use cases most relevant to your environment — safety compliance, occupancy monitoring, queue management, perimeter security, equipment monitoring — and audit your existing camera infrastructure for coverage and quality.
Model Selection & Training Requirements
We evaluate the AI models required for your specific detection tasks — people counting, object detection, behavior recognition, anomaly detection — and assess the training data requirements and customization needed for your environment.
Platform Evaluation
We evaluate video analytics platforms — Avigilon AI, Milestone XProtect AI, BriefCam, Samsara, and others — against your camera ecosystem, use cases, and integration requirements.
Edge vs. Cloud Architecture Design
Video analytics can run at the edge (camera or local server) for low latency and data privacy, or in the cloud for scale and model updates. We design the architecture appropriate for your use cases and constraints.
Trained Video Analytics Selection Criteria
Before committing to any Trained Video Analytics platform, these are the points worth forcing a straight answer on.
Model Accuracy in Your Environment
Video analytics accuracy varies significantly by lighting conditions, camera angle, and scene complexity. Validate detection accuracy in your actual physical environments — not controlled lab conditions.
False Positive Rate
False positives in safety or security applications create alert fatigue and erosion of trust. Evaluate false positive rates in realistic conditions before configuring automated alerts or responses.
Camera Ecosystem Compatibility
Video analytics platforms must work with your existing cameras — manufacturer, resolution, frame rate, compression format. Evaluate compatibility before any platform selection.
Edge Processing Capability
For privacy-sensitive environments or bandwidth-constrained locations, edge processing (on-camera or local server) may be required. Evaluate edge compute requirements against your infrastructure.
Privacy & Data Governance
Video analytics involving people raises significant privacy considerations. Evaluate data retention policies, facial recognition capabilities (and whether you want to enable them), and compliance with applicable privacy regulations.
Integration with Physical Security & Operations Systems
Video analytics value is maximized when insights trigger actions in your access control, incident management, or operations platforms. Evaluate available integrations and API quality.
"RLM brought structure to a process we didn't know how to start. They asked the right questions, surfaced the right vendors, and kept us from making decisions we would have regretted."
We stay involved through implementation, because selection is the easy half.
Where This Matters Most
Sector-specific considerations we see repeatedly in ai and automation engagements.
A Sample of the AI & Automation Providers We Evaluate
RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →
Thinking About Trained Video Analytics?
Start with a no-cost conversation with an RLM AI advisor — vendor neutral, no agenda, just clarity.
Speak to an Advisor