Object Detection and Hand Pose Estimation with Python

Published 6/2024
Created by Ahmet Enes Yalçınkaya
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 15 Lectures ( 1h 10m ) | Size: 526 MB

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.

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