Computer Vision Bootcamp with Python (OpenCV) - YOLO, SSD

About Computer Vision Bootcamp with Python (OpenCV) - YOLO, SSD

This course is about the fundamental concept of image processing, focusing on

face detection

and

object detection

. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to crime investigation. Self-driving cars (for example lane detection approaches) relies heavily on computer vision. With the advent of deep learning and graphical processing units (GPUs) in the past decade it's become possible to run these algorithms even in real-time videos. So what are you going to learn in this course?

Section 1 - Image Processing Fundamentals:

computer vision theory

what are pixel intensity values

_convolution_ and _kernels_(filters)

blur kernel

sharpen kernel

edge detection in computer vision (edge detection kernel)

Section 2 - Serf-Driving Cars and Lane Detection

how to use computer vision approaches in lane detection

_Canny's algorithm_

how to use _Hough transform_ to find lines based on pixel intensities

Section 3 - Face Detection with Viola-Jones Algorithm:

Viola-Jones approach in computer vision

what is _sliding-windows approach_

detecting faces in images and in videos

Section 4 - Histogram of Oriented Gradients (HOG) Algorithm

how to outperform Viola-Jones algorithm with better approaches

how to detects _gradients_ and edges in an image

constructing _histograms_ of oriented gradients

using support vector machines (SVMs) as underlying machine learning algorithms

Section 5 - Convolution Neural Networks (CNNs) Based Approaches

what is the problem with sliding-windows approach

region proposals and _selective search_ algorithms

region based convolutional neural networks (C-RNNs)

fast C-RNNs

faster C-RNNs

Section 6 - You Only Look Once (YOLO v11) Object Detection Algorithm

what is the YOLO approach?

constructing bounding boxes

how to detect objects in an image with a single look?

intersection of union (IOU) algorithm

how to keep the most relevant bounding box with _non-max suppression_?

implementation of YOLO11 with images and videos

training YOLO with custom dataset

Section 7 - Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDD

what is the main idea behind SSD algorithm

constructing anchor boxes

VGG16 and MobileNet architectures

implementing SSD with real-time videos

Section 8 - Object Tracking Algorithms

DeepSORT object detection algorithm

ByteTrack algorithm

BoTSORT algorithm

implementation of object tracking

vehicle counting algorithm We will talk about the theoretical background of

face recognition algorithms

and

object detection

in the main then we are going to implement these problems on a step-by-step basis. Thanks for joining the course,

let's get started!