AI-Powered Smart Parking Management System
Urban campuses and public parking lots face challenges in vehicle authentication, misuse detection, and slot availability monitoring. HUB-AI built and deployed an AI-powered smart parking system integrating facial recognition, license plate recognition, real-time video analytics, and IoT-based slot monitoring — all via a single web-based platform.
60%
Reduction in entry/exit time
90%
Drop in unauthorized access events
< 30 min
Staff monitoring time (down from 4 hours/day)
85–90%
Accuracy on field tests (Face + LPR)
50+
Cameras and 500+ slots supported at scale
Sector
Public Sector
Duration
10 weeks
Team Size
6–8
Model
Concept to Deployment PoC
Region
India
Client Context
Urban campuses and public parking lots face challenges in vehicle authentication, misuse detection, and slot availability monitoring. Manual enforcement is not only expensive, but also inconsistent and reactive.
HUB-AI built and deployed a PoC for an AI-Powered Smart Parking System, integrating facial recognition, license plate recognition (LPR), real-time video analytics, and IoT-based slot monitoring — all delivered via a single web-based platform.
The result: operational efficiency, real-time control, and intelligent vehicle tracking across multiple entry points.
The Challenge
No visibility on unauthorized vehicle entries, manual guard logging, no real-time slot availability, and no alerts on safety violations.
The client managed a large utility facility with 3 parking zones but lacked any intelligent monitoring or automation:
- No visibility on unauthorized or misused vehicle entries
- Manual guards assigned to record staff and visitor logs
- Inability to track parking slot availability in real time
- No alerts on safety rule violations or vehicle overflow
They wanted a modern AI-first solution without investing in proprietary hardware.
Delivery Model
We built a modular, cloud-connected parking solution using computer vision and IoT analytics. The PoC focused on automated entry authentication via face recognition and LPR, parking slot monitoring using edge-based sensors, live dashboards for security supervisors and operations team, and multi-channel alerts on unauthorized entries and over-utilization.
Key capabilities included facial recognition to validate staff and authorized personnel, license plate recognition to auto-detect and track vehicles, smart allocation to suggest or auto-assign available slots, real-time alerts triggered via WhatsApp, Email, and Telegram, and a central web dashboard with role-based access for admins and viewers.
Phase 1 — On-Ground Audit
Mapped entry/exit points and available camera infrastructure. Identified pilot zones and created flow diagrams for entry management.
Phase 2 — Model Deployment & Integration
Configured YOLOv5 for license plate detection. Trained FaceNet on internal employee image datasets. Connected IoT sensor feed to monitor parking bays.
Phase 3 — Web Dashboard & Alerting
Developed real-time dashboard with multi-user access. Configured WhatsApp + Email alerts on intrusions and slot misuse.
Phase 4 — Testing & Feedback
Deployed on 5 cameras across 2 parking zones. Achieved 85%+ accuracy in facial + LPR detection. Average inference time: < 800ms.
Tech Stack
Vision AI Models
Video Ingestion
Backend Infrastructure
UI Layer
Edge IoT Integration
Cloud & Alerts
Business Outcome
The PoC delivered a 60% reduction in entry/exit time, a 90% drop in unauthorized access events, and reduced staff monitoring time from 4 hours per day to under 30 minutes. The system achieved 85–90% accuracy on field tests for both facial and license plate recognition, with architecture ready to support 50+ cameras and 500+ slots.
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