Real-Time Hand Gesture & Traffic Sign Detection with Python, OpenCV & Deep Learning
Welcome to the Real-Time AI Vision Systems Course: Hand Gesture & Traffic Sign Recognition with Python!
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..
Course Content
- Introduction of theTraffic Sign Detection And Recognition System –> 1 lecture • 1min.
- Environment Setup for Python Development –> 2 lectures • 3min.
- Google Colab Setup –> 1 lecture • 2min.
- Packages Installation –> 1 lecture • 1min.
- Dataset Preparation –> 1 lecture • 7min.
- Implementing Utility Functions for Traffic Sign Detection –> 1 lecture • 2min.
- Implementing Loss Function for Traffic Sign Detection –> 1 lecture • 5min.
- EfficientNet-B0 Model Implementation Info –> 1 lecture • 7min.
- Model Training configuration –> 1 lecture • 4min.
- Training the EfficientNet-B0 Model –> 1 lecture • 4min.
- Model Inference –> 1 lecture • 10min.
- Environment Setup for Python Development –> 2 lectures • 3min.
- Hand Gesture Detection and Recognition Overview –> 1 lecture • 1min.
- Setting Up and Exploring Essential Packages –> 1 lecture • 1min.
- Key Variables and Their Role in Hand Gesture Recognition –> 1 lecture • 1min.
- Annotating Frames with Detected Gestures and Landmarks –> 1 lecture • 1min.
- Real-Time Gesture Recognition and Frame Processing –> 1 lecture • 1min.
- Integrating Real-Time Gesture Recognition with Tkinter GUI –> 1 lecture • 2min.
- Tkinter Implementation for Real-Time Hand Gesture Recognition –> 1 lecture • 2min.
- Package Installation for Hand Gesture Recognition System –> 1 lecture • 1min.
- Hand Gesture Recognition Code Execution –> 1 lecture • 2min.
- Wrapping Up –> 1 lecture • 1min.
Requirements
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 & OpenCV and Traffic 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 in gesture-controlled devices, accessibility tools, gaming, and virtual interfaces.
What You’ll Learn – Traffic Sign Detection:
- Set up your Python environment with TensorFlow, OpenCV, and Matplotlib for image preprocessing and visualization.
- Use EfficientNet B0 for 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 to autonomous vehicles, road safety monitoring, and intelligent traffic management.
By the end of this course, you’ll have built two 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!