:raised_hand: :fork_and_knife: A repo for processing the raw hand object detections to produce releasable pickles + library for using these

BSc CS final year project

A real-time hand raise detection application using MediaPipe, OpenCV, and Streamlit. It detects when a user raises their hand in front of a webcam and provides visual feedback. Ideal for interactive educational sessions, meetings, and gesture recognition experiments.

🤖 Finger Icon Detection 🤟 A Python project using OpenCV and MediaPipe to track hand gestures in real-time and overlay custom icons above raised fingers (thumb, index, pinky). Perfect for experimenting with hand tracking and gesture recognition.

- Real-time hand detection using webcam - Counts raised fingers from 0 to 5

HandCount: A real-time hand gesture detection project using MediaPipe and OpenCV to count raised fingers with computer vision.

The Hand Finger Counter project is a real-time hand detection system that counts the number of raised fingers using OpenCV and MediaPipe. The program captures video from a webcam, detects hand landmarks, and uses a simple logic to determine how many fingers are raised.

Real-time student focus and hand-raise detection using OpenCV and MediaPipe Pose. The system tracks posture, head orientation, eye alignment, and slumping to classify attention as Focused or Distracted, draws bounding boxes, detects raised hands, and runs live from a webcam for classroom monitoring.

This project is a Python-based application that uses a webcam to detect hand gestures and perform scrolling actions on the computer. It uses **MediaPipe** for hand detection and **PyAutoGUI** for scrolling. The number of raised fingers determines the scrolling direction

🖐️✨ A real-time face and hand detection system built with Python, MediaPipe, and OpenCV. Ideal for gesture-based apps, smart interfaces, security systems, or educational tools. It tracks facial mesh, counts raised fingers, detects expressions, and shows FPS—bringing interactive computer vision to life!

Developed a real-time Python-based student attendance system using Facenet for facial recognition, SVM for classification, and Haar cascades for anti-spoofing. Integrated hand-raise detection and face login for secure participation tracking.

Real-time Action Detection system for classroom environments using YOLO, FastAPI backend, and React frontend. Detects student and teacher actions such as raising hand, writing, standing, sitting, and interacting, with real-time video stream processing.

Real-time behavior detection system to analyze student engagement in classrooms. Created a custom dataset using LabelImg, performed Fine-Tuning on YOLOv8, and deployed the model to classify behaviors (Focused, Distracted, Sleeping, Raising Hand, Phone) with high accuracy.

AI Virtual Painter using hand tracking technology. Draw in the air with your finger using computer vision and MediaPipe. Features real-time hand detection, gesture-based color selection, and canvas drawing. Simply raise your index finger to draw, use two fingers to select colors (red, green, blue) or eraser. Built with OpenCV, MediaPipe, and Python

The Raised Hand Detection project is a computer vision-based system that detects raised hands in a video stream using OpenCV and Haar cascade classifiers. This project can be used in classrooms, meetings, or online sessions to automatically identify when someone raises their hand.

Raise both hands detection using YOLOv8 pose model

Raise both hands detection using Ultralytics YOLO11 pose model

Reinforcement Learning for Object Detection: PyTorch-based Drone Hand-Raising System

A Hand Raise Detection System from video using YOLOv8, MediaPipe, OpenCV

An android application that performs real-time hand-raise detection using ML Kit Pose Detection and CameraX, built with MVVM architecture. When a raised hand is detected, the app triggers a Text-to-Speech voice response, displays hand-raise status on screen.

Implemented real-time hand detection and finger tracking to trigger musical chords when specific fingers are raised.

Android app for hand raise detection using ML Kit Pose Detection + TTS response (MVVM + Kotlin)

This repository uses Google MediaPipe for hand detection and publishes ROS nodes. When the index finger is raised, it moves forward; when the middle finger is raised, it moves backward; when the thumb is raised, it rotates counterclockwise; and when the little finger is raised, it rotates clockwise.s

Hand tracking software that utilizes media pipes object detection capabilities to track the users hand and number of fingers raised. based on the number of fingers raised the user can control various actions on their computer (it is set up for Mac users only)

Developed a real-time hand tracking system to detect and count the number of raised fingers using computer vision and machine learning. Utilized MediaPipe's hand landmark detection to recognize key hand points and OpenCV for video capture and processing.

This project is a real-time hand tracking application built with Python, leveraging MediaPipe for hand detection and OpenCV for video processing and visualization. It can detect single or multiple hands, identify finger positions, and count the number of fingers raised in real time.

This project is a real-time hand tracking application built with Python, leveraging MediaPipe for hand detection and OpenCV for video processing and visualization. It can detect single or multiple hands, identify finger positions, and count the number of fingers raised in real time.

A smart classroom tool for analyzing student engagement through facial attendance logging and hand-raise detection using computer vision and deep learning.

Real-time computer vision system that detects and counts raised fingers from a hand using **OpenCV image processing techniques**. The system performs background subtraction, hand segmentation, contour analysis, and convex hull detection to estimate the number of visible fingers from a webcam feed.

This project detects and counts fingers in real time using OpenCV for video capture and display, and a deep learning model (MediaPipe Hands) for hand landmark detection. Custom Python logic calculates which fingers are raised for both left and right hands, and displays the results with a clean interface showing counts and FPS.