Computer Vision - Object Detection on Videos - Deep Learning
Quick Starter on Object Detection and Image Classification on Videos using Deep Learning, OpenCV, YOLO and CNN Models
Ratings 4.46 / 5.00
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
Description
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
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
