Object Detection and Hand Pose Estimation with Python

Quick Starter for Object Detection and Hand Pose Estimation using Python


What you'll learn
Learn about the state of the art models in Object Detection and Hand Pose Estimation models.
Have a good understanding of the most powerful Computer Vision models
Understand and implement Computer Vision projects
Master Object Detection and Hand Pose Estimation

Requirements
Basic Python programming skills

Description
Computer Vision(CV)
is one of the de facto
Artificial Intelligence
technology that is present in many
AI application
we come across.
Facial recognition
,
self-driving cars
,
augmented reality
and
many more applications
leverage
computer vision
techniques in some form. Over the past decade,
computer vision
has become more prominent as
AI
applications gain more adoption. The increase in
AI application
adoption contributed to the rise in the number of
computer vision-related jobs and courses.
Learn how to use
OpenCV
,
MediaPipe
and
Python for Computer Vision
in this course. The course shows you how to create two
computer vision
projects. The first involves an
Object Detection
model. The second is a
Hand Pose Estimation
.
MediaPipe Objectron
is a mobile
real-time 3D object detection
solution for everyday objects. It detects objects in 2D images, and estimates their poses through a
machine learning (ML)
model, trained on the
Objectron
dataset.
Object detection
is an extensively studied computer vision problem, but most of the research has focused on 2D object prediction. While 2D prediction only provides 2D bounding boxes, by extending prediction to 3D, one can capture an object’s size, position and orientation in the world, leading to a variety of applications in
robotics
,
self-driving vehicles
,
image retrieval
, and
augmented reality
. Although 2D object detection is relatively mature and has been widely used in the industry,
3D object detection
from 2D imagery is a challenging problem, due to the lack of data and diversity of appearances and shapes of objects within a category.
The ability to perceive the shape and motion of hands can be a vital component in improving the user experience across a variety of technological domains and platforms. For example, it can form the basis for
sign language understanding
and
hand gesture control
, and can also enable the overlay of digital content and information on top of the physical world in augmented reality. While coming naturally to people, robust real-time hand perception is a decidedly challenging
computer vision
task, as hands often occlude themselves or each other (e.g. finger/palm occlusions and hand shakes) and lack high contrast patterns.
Who this course is for:
Anyone interested in artificial intelligence and computer vision

More Info