计算机视觉–视频上的物体检测–深度学习Computer Vision – Object Detection on Videos – Deep Learning

使用深度学习、OpenCV、YOLO和CNN模型对视频进行对象检测和图像分类的快速入门工具

你会学到什么
了解视频上的机器学习，并将其应用于现实世界的问题
学习在Python中实现视频对象检测模型
使用用于图像分类的迁移学习构建您自己的深度学习模型
用于对象检测的更快RCNN、YOLO、HOG和Haar级联的可执行代码
学习实现对象跟踪的排序框架
用于人流量跟踪和自动停车管理的可执行代码

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言：英语+中英文字幕（云桥网络 机译）|大小解压后:1.16 GB |时长:2h 11m


要求
Python中的基本编程技能
描述
**了解如何在对象检测模型的帮助下处理视频并使用图像分类识别视频中所需对象的最佳课程。通过学习对象跟踪，我们将学会开发精彩的应用程序，如人流量跟踪和自动停车管理**

视频上的机器学习有可能对数据驱动的业务产生深远的影响，并正在成为该行业的新流行语。本课程通过视频分析、对象检测和图像分类，提供了对视频的机器学习的端到端覆盖。这是一个完整的实践教程，它教授如何使用捕获、处理和保存视频的3步流程来实施视频分析，了解各种对象检测模型并实施它们以进行社交距离的实时案例研究，最后但并非最不重要的是，深入了解使用深度学习模型、迁移学习和学习如何使用图像分类创建人脸面具检测模型并利用它来实施人脸面具检测解决方案的步骤。

这里只是我们将要学习的几个主题

机器学习

深度学习

视频分析

目标检测

目标检测模型

目标检测算法

基于CNN的图像分类

Python中的图像识别

目标跟踪

简单的在线和实时跟踪(排序)框架

深度学习

深度学习图像分类器

卷积神经网络

迁移学习

Google CoLab上的模型训练

哈尔级联分类器

猪模型

YOLO算法

YOLO V3微型模型

快速R-CNN模型

视频编解码器

Haar Cascade社交距离解决方案，带python代码

使用python代码的Hog Solution社交距离解决方案

使用python代码的YOLO社交距离解决方案

使用python代码实现更快的R-CNN社交距离解决方案

带python代码的人脸面具检测解决方案

用python代码实现人的脚步跟踪

用python代码实现自动停车管理

* * 2021年12月-课程已更新为Ubuntu和Windows平台的工具设置。**

这门课程是给谁的
数据科学的初学者
机器学习专家
愿意过渡到机器学习的开发者
任何希望在视频上实现机器学习的人
任何希望成为更有竞争力的数据科学家的人

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.02 GB | Duration: 2h 11m

Quick Starter on Object Detection and Image Classification on Videos using Deep Learning, OpenCV, YOLO and CNN Models

What you’ll learn
Understand Machine Learning On Videos and apply it to real-world problems
Learn to implement Object Detection Models on Videos in 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
Learn to implement SORT Framework for Object Tracking
Executable Code of SORT for People Footfall Tracking and Automatic Parking Management

Requirements
Basic Programming skills in Python
Description
** Best course for understanding how to work on Videos with the help of Object Detection models and using Image Classification to identify desired Objects in Videos. By learning Object Tracking, we will learn to develop wonderful applications like People Footfall Tracking and Automatic Parking Management **

Machine Learning on Videos has the potential to make a profound impact in a data-driven business and is emerging as the new buzzword in the industry. This course 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, understand various Object Detection Models and 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, Transfer Learning and learn how to create a model on face mask detection using Image Classification and leverage it to implement a solution of face mask detection.

Here are just few of the topics we will be learning

Machine Learning

Deep Learning

Video Analytics

Object Detection

Object Detection Models

Object Detection Algorithm

Image Classification using CNN

Image Recognition in Python

Object Tracking

Simple Online and Realtime Tracking (SORT) Framework

Deep Learning

Deep Learning Image Classifier

Convolution Neural Network (CNN)

Transfer Learning

Model Training on Google CoLab

Haar Cascade Classifier

HOG Model

YOLO Algorithm

YOLO V3 Tiny Model

Faster R-CNN Model

Video codec

Haar Cascade Social Distancing Solution with python code

Hog Solution Social Distancing Solution with python code

YOLO Social Distancing Solution with python code

Faster R-CNN Social Distancing Solution with python code

Face Mask Detection Solution with python code

People Footfall Tracking with python code

Automatic Parking Management with python code

** Dec 2021 – The course has been updated with Tool Setup for both Ubuntu and Windows Platform.**

Who this course is for
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
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