An end‑to‑end TensorFlow/Keras implementation of the YOLO object detection pipeline. Load images, run fast and accurate bounding‑box inference, filter and refine predictions and visualize results side‑by‑side - all organized into a clean, modular workflow.

Face mask detection system that classifies proper, improper, and no mask in real time. Built with MobileNetV2 trained using transfer learning and Flask web interface.

A real-time weapon detection system using Yolov8 to identify firearms or knives in video feeds for enhanced public safety and surveillance automation.

Sign Language Detector is a real-time ISL 🇮🇳 recognition system. It uses OpenCV and MediaPipe for hand tracking, a machine learning classifier for gesture recognition, and a Flask web interface for live inference.

Real-time hand gesture recognition system using MediaPipe & OpenCV. Control mouse, volume, navigation, and play games with hand gestures. Built for a Computer Vision course.

This vehicle identification project utilizes the YOLOv5 deep learning model for detecting and classifying vehicles from images, videos, and live streams. It supports real-time inference, saving outputs with bounding boxes, confidence scores, and class labels, making it ideal for traffic monitoring and smart surveillance systems.

This project demonstrates how to detect human faces in images and real-time webcam streams using Python and OpenCV's Haar Cascade classifier. It supports modern image formats including `.webp`, and displays results using Matplotlib. Ideal for beginners learning computer vision or building small-scale detection systems.

Real-time vehicle detection and counting system using YOLOv8 and OpenCV. Detects cars, motorcycles, buses, and trucks from video footage, counts them per frame, and outputs annotated video results. Ideal for traffic monitoring, smart city projects, and computer vision portfolios.

Real-time yoga pose accuracy detector using MediaPipe and OpenCV. Classifies poses from webcam input with KNN and provides feedback on duration and accuracy, built with Python and scikit-learn.

Real-time violence and weapon detection system using MoBiLSTM and YOLOv8, with optional Telegram alerts.

An advanced, modular lane detection and road perception framework built with OpenCV, NumPy, and Gradio, engineered for real-time autonomous driving research. It features adaptive ROI mapping, probabilistic Hough transformation, EMA-smoothed lane tracking, and an interactive Gradio demo UI.

🎥 Explore real-time video processing and object tracking with OpenCV, featuring interactive controls and basic object tracking for practical computer vision applications.

Arabic sign language recognition system using transfer learning (ResNet101V2) and YOLO-based hand detection. Benchmarks CNN, LSTM, and GNN architectures with multi-metric evaluation framework.

YOLO Object detection with OpenCV and Python.

AeroVision-AI is an advanced aerial intelligence system for real-time vehicle detection, tracking, and analytics from drone footage. Built with YOLOv8, Streamlit, and OpenCV, it offers live inference visualization, CSV-based analytics, dynamic watermarking, and video export, a production-grade showcase of computer vision in motion.

Real-time American Sign Language detection system using CNN, OpenCV, MediaPipe, and PyTorch for computer vision-based sign language recognition

Real-Time Vehicle Speed Detection and Counting System

🎮 Production-ready real-time Rock-Paper-Scissors gesture recognition system using MediaPipe & OpenCV. Features: instant hand tracking (30+ FPS), 95% accuracy, fuzzy matching, TDD methodology with 95% test coverage. Bilingual (EN/繁中). Perfect for CV learning & interactive apps.

Computer vision system with YOLOv3 detection and K-means clustering for vehicle tracking and space management. Features OpenCV preprocessing pipeline, color classification, and statistical analysis dashboard.

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