Technology  Products

Project Summary:
VISIONPARK is a smart urban parking solution that provides comprehensive, real-time data on parking zones and slot availability in cities. Using computer vision and deep learning, the system detects and monitors parking slot occupancy, while GNSS-based localization guides users efficiently to available spaces. The platform is designed to reduce time spent searching for parking, minimize urban congestion, and enhance the overall commuter experience.

Problem Statement:
Urban parking is a persistent challenge in densely populated cities, leading to traffic congestion, fuel wastage, and driver frustration. Traditional parking systems need additional infrastructure, lack real-time updates and fail to guide users effectively to available slots.

Methodology:
VISIONPARK integrates CCTV camera feeds to detect parking slot occupancy using deep learning-based image analysis. Real-time data is processed and mapped to city zones, while GNSS technology calculates the user’s position and distance to each available slot. An intuitive web interface displays occupancy, distances, and navigation guidance for users.

Key Findings & Value Proposition:

1. Provides real-time, accurate detection of parking slot occupancy.

2. Reduces time and fuel wasted while searching for parking.

3. Enhances urban traffic management and commuter convenience.

4. Minimizes infrastructure costs by leveraging existing CCTV cameras for occupancy monitoring.

5. Offers distance-based guidance to available parking zones for efficient navigation

Unique Selling Proposition:
Unlike traditional parking apps, VISIONPARK combines real-time computer vision-based monitoring with deep learning and GNSS navigation, providing precise, actionable insights for urban parking management.

National Impact:
VISIONPARK supports Sustainable Development Goal (SDG) 11 – Sustainable Cities and Communities by reducing traffic congestion, lowering emissions, and enabling efficient, technology-driven urban parking solutions.

The VAINR is a human-friendly AI-powered robot developed by the Computer Vision Lab at IITTNIF. Designed for GNSS-denied environments like offices, commercial buildings, and campuses, the robot welcomes visitors, recognizes faces, and guides them efficiently to their desired locations using advanced computer vision and AI-based navigation technologies.

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Project Summary:

VAINR is a vision-guided indoor navigation robot that enhances visitor experience in GNSS-denied environments. The robot employs face recognition to identify individuals, greets them accordingly, and navigates them to their requested location using AI-driven path planning. It is designed to be human-friendly, interactive, and efficient, offering seamless guidance in complex indoor spaces like offices, colleges, and commercial buildings.

Problem Statement:
Navigating large indoor spaces can be challenging for new visitors and even for existing personnel in unfamiliar areas. Traditional signage or mobile-based guidance is often insufficient, especially in GNSS-denied environments where GPS signals are unavailable.

Methodology:

VAINR combines face recognition, speech-to-text input processing, AI-driven path planning, and computer vision-based navigation to operate effectively in GNSS-denied indoor environments. Upon detecting a person at the entrance, it identifies whether the individual is registered in the system using face recognition and greets them accordingly. For unrecognized users, it interprets their spoken destination using speech-to-text translation, calculates the optimal route using AI algorithms, and navigates while continuously sensing the environment to avoid obstacles. The robot provides real-time, personalized guidance, ensuring a safe, efficient, and human-friendly indoor navigation experience.

Key Findings & Value Proposition:

• Reduces visitor confusion and saves time in indoor navigation.

• Enhances visitor experience through personalized greetings and guidance.

• Operates effectively in GNSS-denied environments.

• Improves efficiency in offices, campuses, and commercial spaces.

Unique Selling Proposition:

Combines face recognition, AI path planning, and interactive human-robot communication in a single platform, providing personalized, real-time indoor navigation in environments where GPS is unavailable.

National Impact:
VAINR supports Sustainable Development Goal (SDG) 11 – Sustainable Cities and Communities by enhancing indoor mobility, accessibility, and efficient navigation, contributing to smarter and more inclusive built environments.

Project Summary:

VISIONGuard aims to streamline and secure the registration process for academic, professional, and skill-development events. The system leverages computer vision and AI algorithms to detect fake, manipulated, or incorrect registration entries by analyzing submitted proofs of payment and other documents. It minimizes human intervention, reduces manual validation time, and ensures that event organizers can efficiently verify legitimate participants

Problem Statement:

Manual verification of registrations for workshops, conferences, and training programs is time-consuming and prone to errors, and fraudulent entries or manipulated documents can lead to misuse of resources and compromised event integrity.

Methodology:  

VisionGuard uses image and document analysis through computer vision to detect inconsistencies, alterations, or fraudulent submissions in registration proofs. AI models classify entries as valid or suspicious, flagging them for review if necessary. The approach automates validation, integrates with event management workflows, and generates reports to help organizers act promptly.

Key Findings & Value Proposition:

• Reduces manual validation time significantly.

• Detects fake or manipulated registration entries accurately.

• Ensures data integrity and fair participation in events.

• Scalable to multiple event types, including seminars, webinars, and training programs.

Unique Selling Proposition: 

Fast, reliable, and automated solution for detecting fraudulent registrations, reducing human error and operational overhead. 

National Impact: 

VisionGuard supports SDG 16 – Peace, Justice, and Strong Institutions by promoting transparent, accountable, and fraud-free event registration processes for educational, professional, and skill-development programs.