Object detection using Dlib, opencv and python

Course Description

Learn to detect object by learning fundamentals of object detection scanning using opencv, dlib and popular programming language Python.

Build a strong foundation in object detection with this tutorial for beginners.

• Understanding of how object detection is done

• Learn basics of training object detection

• Leverage Dlib, OpenCV and Python to detect objects inside image

• User python for programming

• Use step by step instructions along with plenty of examples

• Build a real world application for object detection

• Learn fundamentals of HOG (Histogram of Oriented Gradients) and SVM (Support Vector Machine)

A Powerful Skill at Your Fingertips.  Learning the fundamentals of object detection puts a powerful and very useful tool at your fingertips. Python, opencv and Dlib are free, easy to learn, has excellent documentation.

Object Detection is important process to detect pedestrians in autonomous car driving app and faces in video applications.

Jobs in image processing area are plentiful, and being able to learn dlib, opencv and python will give you a strong edge.

Object detection  tasks are becoming very popular in fortune 500 images. Amazon, Walmart, Google eCommerce websites are few famous example of object detection in action.

Content and Overview  

This course teaches you on how to detect object sin images using opencv, python and dlib framework.  You will work along with me step by step to build following answers

• Introduction to object detection

• Learn how to apply object detection  to caltech image dataset

• Build an python app  step by step using dlib opencv and python and learn how to train model using dlib and then use ut to detect objects in real world images

  
  

What am I going to get from this course?

• Learn fundamentals of object detection from professional trainer from your own desk.

• Over 10 lectures teaching you how to perform image thresholding using dlib, opencv and python

• Suitable for beginner programmers and ideal for users who learn faster when shown.

• Build a real world application to detect object

• Visual training method, offering users increased retention and accelerated learning.

• Breaks even the most complex applications down into simplistic steps.

• Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.