Showing 1–9 of 15 results
Construction Site PPE Compliance Monitoring is an AI-based computer vision project designed to automatically monitor the use of essential Personal Protective Equipment (PPE) at construction sites.
Real-time AI-based driver monitoring system that detects eye closure and yawning using MediaPipe facial landmarks, EAR and MAR analysis. Includes live webcam processing, drowsiness status, facial landmark visualization, and detection counters.
Facial Emotion Detection Using Computer Vision and DeepFace is an AI-based computer vision project that analyzes facial expressions from images, videos, or live camera feeds to identify the apparent emotional state of detected faces.
Detect fire and smoke automatically from CCTV/video streams using Computer Vision and AI. This project is designed to demonstrate how computer vision can be applied for early fire and smoke detection in indoor and outdoor environments.
PCB Visual Inspection Studio is an offline Windows desktop application for automated PCB visual inspection (AOI) using classical computer vision.
Compare a test PCB with a golden reference board to identify missing components, misplaced components, solder-region differences, scratches, and other visible defects.
Barcode & QR Vision Scanner Studio is an offline Windows desktop application for scanning and generating QR codes and 1D barcodes.
Features include live webcam scanning, image and batch scanning, multi-code detection, scan history, CSV/Excel export, and QR/barcode generation.
Real-Time Fall Detection and Alert System Using Computer Vision is an AI-based safety monitoring project designed to detect potential fall incidents from live camera feeds or recorded videos.
Build a real-time AI fitness tracker using Python, MediaPipe, OpenCV, and Tkinter. Detect and count exercises such as push-ups, squats, chest flys, and dumbbell lifts using human pose estimation and movement analysis. Includes source code suitable for learning, customization, and developing fitness-tech applications.
Develop a computer-vision-based surveillance system for detecting potential shoplifting and suspicious activities in real time. The project analyzes video footage to identify unusual movements and behavioral patterns, providing a practical foundation for retail security, loss prevention, and intelligent CCTV monitoring applications.