Projects using PyTorch in Chattogram

Projects using PyTorch in ChattogramProjects using PyTorch in Chattogram

Cover image for AI Shoplifting Detection System: Intelligent

AI Shoplifting Detection System: Intelligent Video Analytics for Retail Loss Prevention Protect your storefront with an automated AI security solution that never sleeps. This project implements a full-stack Computer Vision pipeline capable of monitoring 16+ simultaneous RTSP streams. By utilizing ByteTrack for stable person re-identification and Deep Learning action classifiers, the system detects unauthorized entry into staff zones and alerts management to shoplifting incidents as they happen. A robust, scalable solution for grocery stores and retail outlets looking to modernize their security infrastructure.

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Cover image for AI-Powered PPE Compliance & Safety

AI-Powered PPE Compliance & Safety Monitoring

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Cover image for multi camera ai tracking for

multi camera ai tracking for mask detection and motion analytics Stop monitoring manually. Start automating safety. In high-stakes environmentshospitals, construction sites, and manufacturing plantscompliance isn't optional. I build Industrial-Grade AI Video Analytics that combine real-time Face Mask Detection with advanced Person Movement Tracking to ensure 24/7 safety oversight without human error. The Synergy: Why Both Matter Most developers offer one or the other. I integrate them into a single, high-performance pipeline: Compliance Monitoring: Instant detection of PPE/Face Mask violations with timestamped logging. Behavioral Tracking: Beyond simple detection, I track individual movement paths to identify "High-Risk" behaviors or unauthorized entry into restricted zones. RTSP Scalability: My systems don't just work on one webcam; they are optimized to handle multi-camera RTSP feeds (16+) with zero lag. Key Features of the System: Dual-Stream Intelligence: Real-time Mask/No-Mask classification paired with unique Person IDs (Re-ID). Zone-Aware Analytics: Define specific "Mask-Mandatory Zones" vs. "Common Areas" to reduce false alerts. Motion & Velocity Insights: Track if a person is running, loitering, or entering a hazardous area without prop

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Cover image for yolov11 alpr anpr system with

yolov11 alpr anpr system with rtsp stream and sql database You will get a production-ready AI-powered vehicle monitoring and license plate recognition system designed for real-world environments such as apartments, parking garages, and secure facilities. This solution combines advanced computer vision (YOLO + OCR + tracking) with a modern React dashboard to deliver accurate, real-time vehicle identification and tracking. What sets my work apart is the focus on reliability, privacy, and scalability. The system is optimized to reduce OCR errors using intelligent plate stabilization, ensuring consistent and accurate results even in challenging conditions. All data is processed and stored locally, making it ideal for privacy-sensitive deployments. The dashboard provides live vehicle cards with owner details, parking status, and access control (authorized, visitor, blocked), giving you full visibility and control. The system is modular and can be extended with features like automated gate control, alerts, analytics, and multi-location support. With a strong background in AI and computer vision systems, I build solutions that are not just demos—but ready for real-world production use.

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