Introduction

This course provides a comprehensive exploration of AI-powered image and video analysis, equipping professionals with the latest concepts, tools, and hands-on techniques essential for today’s digital landscape.

Objectives

• Understand the fundamentals of AI in image and video analysis.
• Gain proficiency in leading AI tools and frameworks.
• Apply deep learning methods for real-world visual data challenges.
• Develop skills in object detection, classification, and tracking.
• Evaluate and optimize AI models for accuracy and efficiency.

Course Outlines

Day 1: Foundations of AI in Visual Analysis

• Overview of AI in image and video processing
• Key concepts: computer vision and deep learning
• Types of visual data and annotation methods
• Overview of popular libraries (OpenCV, TensorFlow, PyTorch)
• Setting up your AI image/video analysis environment

Day 2: Image Analysis Techniques

• Image classification with neural networks
• Feature extraction and representation
• Data augmentation and preprocessing
• Hands-on: Training your first image classifier
• Evaluating model performance

Day 3: Object Detection and Segmentation

• Object detection fundamentals (YOLO, SSD, Faster R-CNN)
• Image segmentation approaches
• Building and training detection models
• Post-processing and visualization
• Hands-on: Detecting multiple objects in images

Day 4: Video Analysis and Activity Recognition

• Video data structures and frame extraction
• Object tracking across frames
• Activity and gesture recognition
• Implementing temporal analysis with AI
• Case study: Real-time video monitoring

Day 5: Advanced Topics and Applications

• AI ethics and bias in visual analysis
• Transfer learning and model optimization
• Deploying AI models for production use
• Emerging applications: healthcare, security, media
• Capstone project: End-to-end analysis workflow