Computer Vision-Based End-to-End AI Solution

Project Overview:
This AI-powered Computer Vision solution integrates with CCTV cameras to enable real-time monitoring and analytics for industries like HSE (Health, Safety & Environment), Manufacturing, and Retail (QSR, Gas Stations, Stores). The platform detects safety violations, operational inefficiencies, and workflow optimizations using AI models and provides instant alerts via mobile apps, WhatsApp, and web dashboards.

Problem Statement:
Industries rely on CCTV surveillance, but most monitoring happens retrospectively, making it difficult to prevent accidents or enforce compliance in real time. Additionally, manual SOP adherence is poor, leading to inefficiencies and unsafe work conditions. The retail sector also lacked real-time insights into queue management and customer flow.

Solution:
✔ CCTV Integration with AI-Powered Edge Computing – The Edge Machine (GPU-based server) processes live camera feeds in real time.
✔ Computer Vision-Based AI Models – Detects safety violations (helmets, gloves, compliance checks), SOP adherence, and workflow inefficiencies.
✔ Automated Alerts & Reporting – Generates instant alerts via WhatsApp, SMS, and mobile notifications for quick action.
✔ Web-Based Dashboard for Historical Data – Stores insights for trend analysis, compliance tracking, and operational planning.
✔ Industry-Specific Applications –

HSE: Real-time PPE monitoring & workplace safety compliance.
Operations: AI-driven workflow tracking & process efficiency optimization.
Retail: Customer movement tracking, queue management, and service enhancement.
My Role & Contributions:
✔ Led product strategy and conducted competitive & market research.
✔ Defined AI-based architecture for real-time analytics and alerting.
✔ Collaborated with stakeholders from marketing, sales, and customers for insights.
✔ Designed & implemented roadmap for deployment and scaling.
✔ Oversaw DevOps integration with CI/CD pipelines, Dockerized deployments.
✔ Managed user training, onboarding, and product launch.

Technologies Used:
Computer Vision & AI/ML Models – Object detection, activity recognition.
React & Angular – Web-based analytics dashboards.
Android & iOS – Real-time mobile alerts & notifications.
Cloud Infrastructure – Scalable backend for AI processing.
DevOps (Docker, CI/CD) – Containerized deployments for efficiency.
SendGrid & WhatsApp API – Automated notifications & user alerts.
Impact & Results:
📌 Enhanced Workplace Safety (HSE): Reduced accidents by proactively identifying risks.
📌 Improved Operational Efficiency: AI-driven SOP monitoring optimized workflows, saving costs.
📌 Retail & QSR Optimization: Automated queue management improved service efficiency.
📌 Real-Time Decision Making: Live alerts & analytics reduced response times for safety violations.