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Computer Vision – Object Detection on Videos – Deep Learning

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Computer Vision – Object Detection on Videos – Deep Learning, available at $49.99, has an average rating of 4.35, with 82 lectures, 4 quizzes, based on 82 reviews, and has 435 subscribers.

You will learn about Learn how to implement Video Analytics using Deep Learning concepts Understand how to implement Object Detection Models on Videos using Python Build your own Deep Learning model using Transfer Learning for Image Classification Executable Code of Faster RCNN, YOLO, HOG and Haar Cascade for Object Detection Build a technical solution containing both Object Detection and Image Classification Develop Image Classification Model using InceptionV3 model architecture Learn to implement SORT Framework for Object Tracking Executable Code of SORT for People Footfall Tracking and Automatic Parking Management This course is ideal for individuals who are Beginners to Data Science or Machine Learning Professionals or Developers willing to transition into Machine Learning or Anyone looking to implement Machine Learning on Videos or Anyone looking to become more employable as a Data Scientist It is particularly useful for Beginners to Data Science or Machine Learning Professionals or Developers willing to transition into Machine Learning or Anyone looking to implement Machine Learning on Videos or Anyone looking to become more employable as a Data Scientist.

Enroll now: Computer Vision – Object Detection on Videos – Deep Learning

Summary

Title: Computer Vision – Object Detection on Videos – Deep Learning

Price: $49.99

Average Rating: 4.35

Number of Lectures: 82

Number of Quizzes: 4

Number of Published Lectures: 82

Number of Published Quizzes: 4

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 86

Original Price: $94.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn how to implement Video Analytics using Deep Learning concepts
  • Understand how to implement Object Detection Models on Videos using Python
  • Build your own Deep Learning model using Transfer Learning for Image Classification
  • Executable Code of Faster RCNN, YOLO, HOG and Haar Cascade for Object Detection
  • Build a technical solution containing both Object Detection and Image Classification
  • Develop Image Classification Model using InceptionV3 model architecture
  • Learn to implement SORT Framework for Object Tracking
  • Executable Code of SORT for People Footfall Tracking and Automatic Parking Management

Who Should Attend

  • Beginners to Data Science
  • Machine Learning Professionals
  • Developers willing to transition into Machine Learning
  • Anyone looking to implement Machine Learning on Videos
  • Anyone looking to become more employable as a Data Scientist

Target Audiences

  • Beginners to Data Science
  • Machine Learning Professionals
  • Developers willing to transition into Machine Learning
  • Anyone looking to implement Machine Learning on Videos
  • Anyone looking to become more employable as a Data Scientist

Welcome to the course “Object Detection on Videos – Deep Learning” that provides an end-to-end coverage of Machine Learning on videos through Video analytics, Object Detection and Image Classification. It is a complete hands-on tutorial that teaches how to implement Video Analytics using the 3-step process of Capture, Process and Save Video. This course helps you to understand various Object Detection Models as well as teach how to implement them for a real-time case study of Social Distancing and last but not the least, take a deep dive into steps involved in using Deep Learning Models and Transfer Learning. By the end of the course, you will also learn how to create a model on face mask detection using Image Classification and leverage it to implement a solution of face mask detection.

This course is a must-have for all the developers in machine learning domain because of:

  • Dedicated In-Course Support is provided within 24 hours for any issues faced

  • Line-By-Line Code Walkthrough for object detection implementation on videos and training a model for image classification

  • Comprehensive Coverage of Object Detection and Image Classification Models

  • Working source code for People Footfall Tracking and Automatic Parking Management project

Here is the list of  key topics and projects we will be learning:

  • Video Analytics Architecture

  • Euclidean Distance

  • Object Detection Models – Haar Cascade, HOG, Faster RCNN, R-FCN, SSD, YOLO

  • Object Detection Model Implementation on Videos with Haar Cascade, HOG and YOLO

  • Image Classification

  • Training Image Classification Model on Google Colab

  • Image Classification Implementation on Videos with Trained InceptionV3 Model

  • Object Tracking with SORT Framework

  • People Footfall Tracking Solution

  • Automatic Parking Management Solution

Course Curriculum

Chapter 1: Course Starter

Lecture 1: Learning Path

Lecture 2: Course Starter

Lecture 3: Udemy Review

Chapter 2: Introduction to Video Architecture and Use Cases

Lecture 1: Objectives

Lecture 2: Video Analytics Overview

Lecture 3: Video Analytics Architecture

Lecture 4: Video Analytics Use Cases Part – 1

Lecture 5: Video Analytics Use Cases Part – 2

Chapter 3: Video Analytics and Processing with Codec

Lecture 1: Objectives

Lecture 2: Capture Video

Lecture 3: Processing Video – get/set method

Lecture 4: Processing Video – read method

Lecture 5: Processing Video – waitKey method

Lecture 6: Processing Video – grab/retrieve method

Lecture 7: Video Codec

Lecture 8: Video Codec Timeline

Lecture 9: FourCC

Lecture 10: Save Video

Chapter 4: Object Detection – Human Detection with Euclidean Distance

Lecture 1: Objectives

Lecture 2: Object Detection Models

Lecture 3: Human Detection

Lecture 4: Euclidean Distance

Chapter 5: Object Detection Models – Haar Cascade, HOG, Faster RCNN, R-FCN, SSD, YOLO

Lecture 1: Objectives

Lecture 2: Haar Cascade Classifier

Lecture 3: HOG Model

Lecture 4: RCNN and Fast RCNN Model

Lecture 5: Faster RCNN and R-FCN Model

Lecture 6: SSD AND YOLO Model

Lecture 7: YOLOv3 and YOLOv3 Tiny Model

Chapter 6: Object Detection Implementation on Videos using Haar Cascade, HOG and YOLO

Lecture 1: Objectives

Lecture 2: Introduction

Lecture 3: Tools Setup – Ubuntu

Lecture 4: Tools Setup – Windows

Lecture 5: Code Walkthrough for Haar Cascade Model – 1

Lecture 6: Code Walkthrough for Haar Cascade Model – 2

Lecture 7: Code Walkthrough for Haar Cascade Model – 3

Lecture 8: Demo Video

Lecture 9: Download Code For Haar Cascade

Lecture 10: Code Changes For Hog Model

Lecture 11: Download Code For HOG Model

Lecture 12: Code Walkthrough for YOLOv3 Tiny – Part 1

Lecture 13: Code Walkthrough for YOLOv3 Tiny – Part 2

Lecture 14: Code Walkthrough for YOLOv3 Tiny- Part 3

Lecture 15: Code Walkthrough for YOLOv3 Tiny – Part 4

Lecture 16: Download Code for Yolov3 Tiny Solution

Lecture 17: Using PyCharm for Coding

Lecture 18: Code Walkthrough for Faster R-CNN

Lecture 19: Download Code For Faster R-CNN Solution

Chapter 7: Training Image Classification Model using Deep Learning on Google Colab

Lecture 1: Objectives

Lecture 2: Image Classification

Lecture 3: Deep Learning Image Classifier – Part 1

Lecture 4: Deep Learning Image Classifier – Part 2

Lecture 5: Transfer Learning with Pretrained CNN

Lecture 6: Google CoLab Setup

Lecture 7: Using Google Colab

Lecture 8: Code Walkthrough For Model Training on CoLab Part – 1

Lecture 9: Code Walkthrough For Model Training on CoLab Part – 2

Lecture 10: Code Walkthrough For Model Training on CoLab Part – 3

Lecture 11: Code Walkthrough For Model Training on CoLab Part – 4

Lecture 12: Code Walkthrough For Model Training on CoLab Part – 5

Lecture 13: Code Walkthrough For Model Training on CoLab Part – 6

Lecture 14: Code Walkthrough For Model Training on CoLab Part – 7

Lecture 15: Code Walkthrough For Model Training on CoLab Part – 8

Lecture 16: Code Walkthrough For Model Training on CoLab Part – 9

Lecture 17: Download Code

Chapter 8: Image Classification Implementation on Videos using Trained Inception V3 Model

Lecture 1: Objectives

Lecture 2: Face Mask Detection

Lecture 3: Tools Setup

Lecture 4: Code Walkthrough -Face Mask Detection Part1

Lecture 5: Code Walkthrough -Face Mask Detection Part2

Lecture 6: Code Walkthrough -Face Mask Detection Part3

Lecture 7: Code Walkthrough -Face Mask Detection Part4

Lecture 8: Code Walkthrough -Face Mask Detection Part5

Lecture 9: Code Walkthrough -Face Mask Detection Part6

Lecture 10: Download Code

Chapter 9: Object Tracking using SORT Framework

Lecture 1: Objectives

Lecture 2: Object Tracking

Lecture 3: SORT Framework

Lecture 4: Tools Setup

Lecture 5: Code Walkthrough – People Footfall Tracking

Lecture 6: Download Code

Chapter 10: Bonus Section

Lecture 1: Bonus Lecture

Instructors

Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 7 votes
  • 3 stars: 8 votes
  • 4 stars: 15 votes
  • 5 stars: 49 votes

Frequently Asked Questions


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You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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