English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 107 lectures (10h 0m) | 4.04 GB

Viola-Jones method, HOG features, R-CNNs, YOLO and SSD (Single Shot) Object Detection Approaches with Python and OpenCV

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?

What you’ll learn

- Have a good understanding of the most powerful Computer Vision models
- Understand OpenCV
- Understand and implement Viola-Jones algorithm
- Understand and implement Histogram of Oriented Gradients (HOG) algorithm
- Understand and implement convolutional neural network (CNN) related computer vision approaches
- Understand and implement YOLO (You Only Look Once) algorithm
- Single Shot MultiBox Detection SDD algorithm
- Master face detection and object detection

## Table of Contents

**Introduction**

1 Introduction

**Convolutional Neural Networks (CNNs) Based Approaches**

2 The standard convolutional neural network (CNN) way

3 Region proposals and convolutional neural networks (CNNs)

4 Detecting bounding boxes with regression

5 What is the Fast R-CNN model

6 What is the Faster R-CNN model

7 Original academic research articles

**You Only Look Once (YOLO) Algorithm Theory**

8 What is the YOLO approach

9 YOLO algorithm – grid cells

10 YOLO algorithm – intersection over union

11 How to train the YOLO algorithm

12 YOLO algorithm – loss function

13 YOLO algorithm – non-max suppression

14 Why to use the so-called anchor boxes

15 Original academic research article

**You Only Look Once (YOLO) Algorithm Implementation**

16 YOLO algorithm implementation I

17 YOLO algorithm implementation II

18 YOLO algorithm implementation III

19 YOLO algorithm implementation IV

20 YOLO algorithm implementation V

21 YOLO algorithm implementation VI

22 YOLO algorithm implementation VII

**Single-Shot MultiBox Detector (SSD) Theory**

23 What is the SSD algorithm

24 Basic concept behind SSD algorithm (architecture)

25 Bounding boxes and anchor boxes

26 Feature maps and convolution layers

27 Hard negative mining during training

28 Regularization (data augmentation) and non-max suppression during training

29 Original academic research article

**SSD Algorithm Implementation**

30 SSD implementation I

31 SSD implementation II

32 SSD implementation III

33 SSD implementation IV

34 SSD implementation V

**Appendix #1 – Neural Networks Theory**

35 Artificial neural networks – inspiration

36 Backpropagation explained

37 Applications of neural networks I – character recognition

38 Applications of neural networks II – stock market forecast

39 Types of neural networks

40 Artificial neural networks – layers

41 Artificial neural networks – the model

42 Why to use activation functions

43 Neural networks – the big picture

44 Using bias nodes in the neural network

45 How to measure the error of the network

46 Optimization with gradient descent

47 Gradient descent with backpropagation

**Appendix #2 – Deep Neural Networks Theory**

48 Deep neural networks

49 Activation functions revisited

50 Loss functions

51 Gradient descent stochastic gradient descent

52 Hyperparameters

**Appendix #3 – Convolutional Neural Networks (CNNs)**

53 Convolutional neural networks basics

54 Feature selection

55 Convolutional neural networks – kernel

56 Convolutional neural networks – kernel II

57 Convolutional neural networks – pooling

58 Convolutional neural networks – flattening

59 Convolutional neural networks – illustration

**Appendix #4 – Support Vector Machines (SVMs)**

60 What are Support Vector Machines (SVMs)

61 Linearly separable problems

62 Non-linearly separable problems

63 Kernel functions

**COURSE MATERIALS (DOWNLOADS)**

64 Download source code

65 Download slides

**Environment Setup**

66 Installing Python and PyCharm on Mac

67 Installing OpenCV

68 Installing Python and PyCharm on Windows

**History of Computer Vision**

69 Evolution of computer vision related algorithms

**Handling Images and Pixels**

70 Images and pixel intensities

71 Handling pixel intensities I

72 Handling pixel intensities II

73 Why convolution is so important in image processing

74 Image processing – blur operation

75 Image processing – edge detection kernel

76 Image processing – sharpen operation

**Computer Vision Project I – Lane Detection Problem (Self-Driving Cars)**

77 Lane detection – the problem

78 Lane detection – handling videos

79 Lane detection – first transformations

80 What is Canny edge detection

81 Getting the useful region of the image – masking

82 Detecting lines – what is Hough transformation

83 Hough transformation illustration

84 Drawing lines on video frames

85 Testing lane detection algorithm

**Viola-Jones Face Detection Algorithm Theory**

86 Face detection problem introduction

87 Viola-Jones algorithm

88 Haar-features

89 Integral images

90 Boosting in computer vision

91 Cascading

92 Original academic research articles

**Face Detection with Viola-Jones Method Implementation**

93 Face detection implementation I – CascadeClassifier

94 Face detection implementation II – CascadeClassifier parameters

95 Face detection implementation III – tuning the parameters

96 Face detection implementation IV – detecting faces real-time

**Histogram of Oriented Gradients (HOG) Algorithm Theory**

97 Histogram of oriented gradients basics

98 Histogram of oriented gradients – gradient kernel

99 Histogram of oriented gradients – magnitude and angle

100 Histogram of oriented gradients – normalization

101 Histogram of oriented gradients – big picture

102 Original academic research article

**Histogram of Oriented Gradients (HOG) Implementation**

103 Showing the HOG features programatically

104 Face detection with HOG implementation I

105 Face detection with HOG implementation II

106 Face detection with HOG implementation III

107 Face detection with HOG implementation IV

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