AI Hand Gesture & Traffic Sign Detection with Python & CV

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.

Description
Welcome to the Real-Time AI Vision Systems Course: Hand Gesture & Traffic Sign Recognition with Python!

In this comprehensive hands-on course, you'll master two powerful real-time AI systems:Hand Gesture Recognition using MediaPipe & OpenCVandTraffic Sign Detection using EfficientNet B0 & TensorFlow. Whether you're building gesture-controlled apps or intelligent transportation tools, this course equips you with the skills to bring AI into everyday applications.

What You'll Learn – Hand Gesture Recognition:

Set up your Python environment and install essential libraries like OpenCV and MediaPipe for gesture recognition tasks.

Learn the fundamentals of hand gesture recognition and its applications in HCI, accessibility, gaming, and device control.

Recognize gestures such as"Thumbs Down","Victory","Thumbs Up","Pointing","Fist Closed", and"Open Palm"in real-time video streams.

Preprocess live video feeds for efficient gesture detection with minimal latency.

Build a full gesture recognition pipeline to detect and classify hand movements frame-by-frame.

Visualize recognition results with gesture labels, bounding boxes, and confidence scores.

Tackle real-world challenges like lighting changes, occlusions, and gesture variation.

Optimize the pipeline for smooth real-time performance and responsive interactions.

Explore use cases ingesture-controlled devices,accessibility tools,gaming, andvirtual interfaces.

What You'll Learn – Traffic Sign Detection:

Set up your Python environment with TensorFlow, OpenCV, and Matplotlib for image preprocessing and visualization.

UseEfficientNet B0for traffic sign classification, balancing speed and accuracy for real-time applications.

Preprocess traffic sign images with normalization, resizing, and data augmentation techniques.

Train and fine-tune the model to improve accuracy and handle imbalanced or noisy datasets.

Implement real-time inference with overlays displaying traffic sign labels and meanings.

Overcome practical challenges like lighting changes, occlusions, and low-resolution inputs.

Deploy your model into real-time systems for driver assistance or smart transportation use cases.

Apply your system toautonomous vehicles,road safety monitoring, andintelligent traffic management.

By the end of this course, you'll have builttwo fully functional AI-powered systems: one for recognizing hand gestures and another for detecting traffic signs in real-time. You'll be ready to integrate these technologies into innovative applications that enhance interactivity, safety, and automation.

Whether you're a student, professional, developer, or AI enthusiast, this step-by-step course will equip you to build impactful real-world solutions.Enroll today and start your journey into the world of AI-powered computer vision!

Who this course is for:
- Students looking to dive into AI and apply it to real-world problems like hand gesture recognition using MediaPipe and traffic sign detection using EfficientNet B0.nition using mediapipe library.
- Working professionals aiming to upskill in AI, Machine Learning, and Python programming for image classification, real-time detection, and practical applications.
- IoT and Embedded System enthusiasts who want to integrate AI into IoT solutions, including gesture control and smart transportation systems.
- Autonomous vehicle and smart transportation enthusiasts interested in AI-powered traffic sign recognition and intelligent driver assistance systems.
- Aspiring developers aiming to build a career in AI, machine learning, computer vision, or deep learning through hands-on training in model development and deployment.
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