Computer Vision by using C++ and OpenCV with GPU support

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Overview

Chapter 2: Set up Necesssary Environments

Lecture 1: Driver installation

Lecture 2: Cuda toolkit installation

Lecture 3: Compile OpenCV from source with CUDA support part-1

Lecture 4: Compile OpenCV from source with CUDA support part-2

Lecture 5: Python environment for flownet2-pytorch

Chapter 3: Introduction with a few basic examples!

Lecture 1: Read camera & files in a folder (C++)

Lecture 2: Edge detection (C++)

Lecture 3: Color transformations (C++)

Lecture 4: Using a trackbar (C++)

Lecture 5: Image filtering with CUDA (Introduction to using OpenCV GPU methods on C++)

Chapter 4: Background segmentation

Lecture 1: Background segmentation with MOG (C++)

Lecture 2: MOG and MOG2 cuda implementation (C++ – cuda)

Lecture 3: Special app: Track class

Lecture 4: Special app: Track bgseg Foreground objects

Chapter 5: Object detection with openCV ML module (C++ CUDA)

Lecture 1: A simple application to prepare dataset for object detection (C++)

Lecture 2: Train model with openCV ML module (C++ and CUDA)

Lecture 3: Object detection with openCV ML module (C++ CUDA)

Chapter 6: Optical Flow

Lecture 1: Optical flow with Farneback (C++)

Lecture 2: Optical flow with Farneback (C++ CUDA)

Lecture 3: Optical flow with Nvidia optical flow SDK (C++ CUDA)

Lecture 4: Optical flow with Nvidia Flownet2 (Python)

Lecture 5: Performance Comparison

Chapter 7: Bonus/Different Examples

Lecture 1: Moving Camera Motion Estimation

Lecture 2: Warping via CUDA implementation

Lecture 3: Foreground object detection with feature comparison

Lecture 4: Custom object detection with CNN based features