| 1 -Annotating Frames with Gesture and Hand Landmarks.mp4 | 13.9 MB | ||
| 1 -Capturing Frames and Updating Tkinter Window in Real-Time.mp4 | 19.1 MB | ||
| 1 -Course Introduction and Features.mp4 | 23.6 MB | ||
| 1 -Course Wrap-Up.mp4 | 9.1 MB | ||
| 1 -Dataset Preparation.mp4 | 78.2 MB | ||
| 1 -EfficientNet-B0 Model Implementation Info.mp4 | 76 MB | ||
| 1 -Explanation of Required Packages for Hand Gesture Recognition.mp4 | 8.1 MB | ||
| 1 -Google Colab Setup.mp4 | 20.6 MB | ||
| 1 -Hand Gesture Detection and Recognition Project Overview.mp4 | 10.2 MB | ||
| 1 -Hand Gesture Recognition Code Execution.mp4 | 24.4 MB | ||
| 1 -Installing Python.mp4 | 15.2 MB | ||
| 1 -Loss function.mp4 | 55.3 MB | ||
| 1 -Model Inference.mp4 | 125.3 MB | ||
| 1 -Model Training configuration.mp4 | 51 MB | ||
| 1 -Package Installation for Hand Gesture Recognition System.mp4 | 10 MB | ||
| 1 -Packages Installation.mp4 | 12.4 MB | ||
| 1 -Real-Time Gesture Recognition and Frame Annotation.mp4 | 13.4 MB | ||
| 1 -Tkinter Implementation for Real-Time Hand Gesture Recognition.mp4 | 19.9 MB | ||
| 1 -Training the EfficientNet-B0 Model.mp4 | 48.9 MB | ||
| 1 -Understanding and Customizing Key Variables in Hand Gesture Recognition.mp4 | 12.6 MB | ||
| 1 -Utils Function.mp4 | 26.3 MB | ||
| 2 -VS Code Setup for Python Development.mp4 | 19.2 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Traffic_Sign_Detection_and_Recognition.ipynb | 1.1 MB | ||
| gesture_recognition.py | 5.9 KB | ||
| gesture_recognizer.task | 8 MB | ||
| pointing_out.png | 5.6 KB | ||
| requirementx.txt | 0 B | ||
| thumbs_down.png | 5.7 KB | ||
| thumbs_up.jpg?042148 | 46.4 KB | ||
| traffic sign test 1.jpg?042148 | 45.5 KB | ||
| traffic sign test 2.jpg?042148 | 32.7 KB | ||
| traffic sign test 3.jpg?042148 | 42.9 KB | ||
| victory.jpeg | 12.2 KB | ||
| ▲ 37 total files | |||
AI Hand Gesture & Traffic Sign Detection with Python & CV
https://WebToolTip.com
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 3m | Size: 733 MB
Real-Time Hand Gesture & Traffic Sign Detection with Python, OpenCV & Deep Learning
What you'll learn
Learn hand gesture and traffic sign recognition for HCI, accessibility, road safety, and autonomous systems.
Set up Python with OpenCV, MediaPipe, TensorFlow for real-time gesture and sign recognition.
Explore gesture and traffic sign detection for control systems, virtual interfaces, and smart transportation.
Detect hand gestures using MediaPipe and classify signs using EfficientNet B0 in real-time.
Recognize gestures like Thumbs Up, Victory, Fist, and classify traffic signs with ML and landmarks.
Preprocess images and videos using resizing, normalization, and augmentation for better model input.
Visualize results with labels, confidence scores, and bounding boxes for easy interpretation.
Train EfficientNet B0 on traffic signs and optimize hyperparameters to improve accuracy.
Handle issues like lighting, occlusions, and low resolution for robust real-world performance.
Use gesture recognition for control, gaming, and accessibility; use sign detection for driving and safety.
Integrate both systems into real-time apps for smart interaction and decision-making.
Requirements
Basic understanding of Python programming (helpful but not mandatory).
A laptop or desktop computer with internet access[Windows OS with Minimum 4GB of RAM).
No prior knowledge of AI or Machine Learning is required—this course is beginner-friendly.
Enthusiasm to learn and build practical projects using AI and IoT tools.
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