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.
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.
Every engagement follows a structured process — from discovery and vendor evaluation to pilot design and scale — adapted to the specific constraints and maturity of your organization.
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.
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.
We evaluate video analytics platforms — Avigilon AI, Milestone XProtect AI, BriefCam, Samsara, and others — against your camera ecosystem, use cases, and integration requirements.
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.
These are the evaluation dimensions that consistently separate successful deployments from expensive pilots that never reach production scale.
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 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.
Video analytics platforms must work with your existing cameras — manufacturer, resolution, frame rate, compression format. Evaluate compatibility before any platform selection.
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.
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.
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."
"What set RLM apart was that they didn't have a preferred answer. They evaluated our options honestly and told us what they actually thought."
Start with a no-cost conversation with an RLM AI advisor — vendor neutral, no agenda, just clarity.
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