Edge AI for Computer Vision: Real-Time Image Processing

• Familiarity with computer vision concepts
• Experience with Python and deep learning frameworks
• Basic knowledge of edge computing and IoT devices

Audience

• Computer vision engineers
• AI developers
• IoT professionals

Edge AI for Computer Vision is revolutionizing real-time image and video analysis by enabling AI models to run directly on edge devices, reducing latency and improving efficiency.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level computer vision engineers, AI developers, and IoT professionals who wish to implement and optimize computer vision models for real-time processing on edge devices.

By the end of this training, participants will be able to:

• Understand the fundamentals of Edge AI and its applications in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Use frameworks like TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models for performance, power efficiency, and low-latency inference.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Edge AI for Computer Vision

• Overview of Edge AI and its benefits
• Comparison: Cloud AI vs Edge AI
• Key challenges in real-time image processing

Deploying Deep Learning Models on Edge Devices

• Introduction to TensorFlow Lite and OpenVINO
• Optimizing and quantizing models for edge deployment
• Case study: Running YOLOv8 on an edge device

Hardware Acceleration for Real-Time Inference

• Overview of edge computing hardware (Jetson, Coral, FPGAs)
• Leveraging GPU and TPU acceleration
• Benchmarking and performance evaluation

Real-Time Object Detection and Tracking

• Implementing object detection with YOLO models
• Tracking moving objects in real-time
• Enhancing detection accuracy with sensor fusion

Optimization Techniques for Edge AI

• Reducing model size with pruning and quantization
• Techniques for reducing latency and power consumption
• Edge AI model retraining and fine-tuning

Integrating Edge AI with IoT Systems

• Deploying AI models on smart cameras and IoT devices
• Edge AI and real-time decision-making
• Communication between edge devices and cloud systems

Security and Ethical Considerations in Edge AI

• Data privacy concerns in edge AI applications
• Ensuring model security against adversarial attacks
• Compliance with AI regulations and ethical AI principles

Summary and Next Steps