AI-Powered Video Surveillance as a Service (VSaaS)
Security teams at industrial campuses and logistics facilities are overwhelmed by manual monitoring of dozens of surveillance feeds. HUB-AI developed and deployed a web-based VSaaS platform enhanced with real-time AI to automate incident detection, PPE compliance monitoring, and alert management across multiple sites.
60%
Improvement in PPE compliance rate (first 30 days)
< 5 min
Security incident response time (down from hours)
75%
Reduction in manual surveillance time per officer
85–90%
Facial + PPE detection accuracy
100+
Cameras supported at scale (from 10)
Sector
Manufacturing
Duration
12 weeks
Team Size
6–10
Model
Rapid PoC Build with Full-Stack Deployment
Region
India
Client Context
Security teams at industrial campuses and logistics facilities are often overwhelmed by manual monitoring of dozens of surveillance feeds. Inconsistent compliance with PPE (personal protective equipment) protocols and unauthorized access pose recurring safety and liability risks.
HUB-AI developed and deployed a web-based Video Surveillance as a Service (VSaaS) platform enhanced with real-time AI to automate incident detection, alert management, and compliance reporting across multiple sites.
The Challenge
Dozens of camera feeds required constant manual supervision with no real-time detection of PPE violations or restricted access breaches.
The client operated a network of warehouses and production facilities where safety was a top priority. However:
- Dozens of camera feeds required constant manual supervision
- No real-time detection of PPE violations or restricted access breaches
- Incident reporting was delayed and lacked context or evidence
- Increased compliance audits were stressing the internal HSE team
The client sought a modular, AI-first solution that could integrate with existing camera infrastructure and scale.
Delivery Model
We built a PoC-ready surveillance platform combining real-time AI models, web-based camera management, and multi-channel alerting workflows. Key capabilities included facial recognition for known staff and unauthorized person detection, PPE compliance detection for helmets, safety vests, and goggles, intrusion and motion alerts in restricted zones, an event logging system with snapshot archiving and time-stamps, and a web-based dashboard for live monitoring, admin control, and audit trail access.
Phase 1 — Feasibility & Site Study
Analyzed 20+ camera feeds from 3 zones. Validated performance using 2 public datasets + internal footage samples.
Phase 2 — AI Model Tuning
Trained FaceNet on employee photo database. Created custom detection model for helmets, goggles, vests. Achieved >88% PPE detection accuracy across variable light conditions.
Phase 3 — Platform Development
Developed dashboard with multi-user login and camera-level controls. Enabled configurable detection rules per camera zone.
Phase 4 — Real-Time Alerting + Logging
Configured alert thresholds for specific hours and zones. Alert delivery via WhatsApp, email, and Telegram. Logged all flagged events with image + timestamp in admin panel.
Tech Stack
AI Models
Video Processing
Backend Services
Frontend
Alerts & Messaging
Cloud Deployment
Business Outcome
The platform improved PPE compliance rate by 60% within the first 30 days. Security incident response time was reduced from hours to under 5 minutes. Manual surveillance time was cut by 75% per security officer. The system achieved 85–90% accuracy for facial and PPE detection and was designed to scale from 10 to 100+ cameras.
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