Workplace Safety AI

Real-Time Violence Prevention

Secure your facility lobby, reception, and retail checkout lanes. Detect physical aggression, striking gestures, and posture anomalies instantly using local, private edge pose estimation on existing security cameras.

Security Aggression Detection interface
Local Edge Pose VLM Active Response Speed: 180ms
< 500ms
Escalation Alert Time
3 Types
Aggression Signals Tracked
100%
Local Data Residency
Zero
Biometric Privacy Violations
Operational Danger

The Blind Spot of CCTV Auditing

Traditional security networks capture footage but do not trigger early response alerts until physical altercations have already escalated.

Monitoring Fatigue

Guards cannot watch dozens of camera streams concurrently. Aggressive encounters in lobbies or elevators go unnoticed until it is too late to de-escalate.

Causes delayed response times

Biometric Privacy Bans

Facial recognition violates strict corporate privacy rules and public data compliance, leading to union complaints and legal liability.

Triggers legal compliance risks

Expensive Cloud Routing

Streaming 1080p security camera feeds continuously to cloud AI servers consumes excessive bandwidth and exposes video logs to external internet leaks.

Consumes massive internet data
Technical Architecture

How It Works Under the Hood

Real-time video analytics use local edge pose estimation models to analyze joint geometry without identifying individuals.

1

CCTV Stream Capture

Continuous RTSP video stream capture directly from existing lobby, elevator, or reception cameras.

2

Skeletal Pose Tracker

A lightweight edge computer vision model maps 17 key skeletal joints, identifying sudden arm raises, lunging postures, or aggressive stances.

3

Instant Console Alert

Aggression warnings are sent within 500ms to local security panels, triggering pre-escalation interventions.

Want to see this running on your existing camera feeds?

Send us a short 30-second security clip and we will return an analyzed pose landmark overlay report.

Request a Free Detection Analysis
Operational Scenarios

Securing Your Facilities

Implement proactive security alerts in high-risk zones without compromising individual biometric identities.

ATM Lobbies

Detect aggressive physical gestures or sudden altercations in un-staffed bank teller spaces to alert patrol vans immediately.

Reception Desks

Monitor front reception desks in corporate offices to trigger silent alert panels if dynamic body movements indicate distress.

Retail Checkouts

Identify hostile gestures towards checkout staff in high-volume supermarkets, permitting managers to intervene before situations escalate.

Elevator Security

Flag sudden struggle or physical confinement inside building lifts, triggering immediate audio intercom connection from safety desks.

Joint Landmark Auditing

Structured Pose Metadata

Local Processing

Runs entirely on local edge hardware with zero reliance on cloud streaming.

Detailed Pose Mapping

Identifies 17 joints, allowing gesture tracking without facial verification or individual profiling.

Zero Privacy Exploits

Skeletal coordinates extract no facial parameters or individual metadata, guaranteeing 100% anonymized compliance.

aggression-telemetry.json
{
  "status": "alert_active",
  "timestamp": 1783058428,
  "camera_id": "lobby_elevator_2",
  "detections": [
    {
      "person_id": 421,
      "pose_class": "physical_aggression",
      "confidence": 0.941,
      "keypoints": {
        "right_wrist": [420, 310],
        "right_shoulder": [380, 420],
        "head": [390, 250]
      },
      "joint_velocity_score": 8.7
    }
  ]
}
FAQ

Frequently Asked Questions

No. The software maps only the skeletal joint coordinates (17 keypoints) to analyze movement patterns. It does not perform facial recognition or log individual identities, aligning fully with data privacy laws.
Yes. The edge software hooks into any standard ONVIF or RTSP camera network streams, requiring no changes to your current security infrastructure.
The AI analyzes joint velocity and posture history across continuous frames. Superficial actions like greetings, waves, or running are filtered out automatically by temporal smoothing layers.
No. The system runs fully local on an edge server located in your facility's server room. Visual feeds are processed inside your firewalled network with zero cloud transmission.
The local controller integrates directly with security console monitors, mobile messaging interfaces, VMS platforms, or physical buzzers to alert teams immediately.

Detect Escalations Early. Secure Your Facilities.

Eliminate response bottlenecks. Discuss edge computer vision integrations for your security network.

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