siam mask object tracking and segmentation in OpenCV python: Using Python and OpenCV, implement real-time Object Tracking and Segmentation
What you’ll learn in siam mask object tracking
- Object Tracking with Segmentation
- Fundamentals of Siam Mask
- How to set-up your programming environment
- How to work with your own Dataset
- Train Siam Mask For your own Applications
- How to test if Siam Mask is working
Requirements needed for siam mask object tracking
- Python Programming Experience
- PC or Laptop
- Nvidia CUDA enabled – GPU (Optional)
- OpenCV Experience
What Is Siam Mask
By implementing both real-time object tracking and semi-supervised video object segmentation with a single method, this Siam Mask Object Tracking and Segmentation in OpenCV Python course will teach you how to implement both tasks. Adding a binary segmentation task to the loss function in SiamMask improves the offline training procedure of popular partially-convolutional Siamese approaches for object tracking.
After training, SiamMask relies only on a single bounding-box initialization and operates online, producing class-agnostic (any class will work) object segmentation masks and rotated bounding boxes at 35 frames per second.
Our simple, versatile, and fast approach allows us to establish a new state-of-the-art among real-time trackers on the VOT-2018 dataset while also demonstrating competitive performance and the best speed on DAVIS-2016 and DAVIS-2017 for video object segmentation.
Applications of Siam Mask
- Automatic Data Annotation – Regardless of Class
- Object Detection and targeting
- Virtual Background without Green Screen
What you will Learn?
In this Siam Mask Object Tracking and Segmentation in OpenCV Python course, you will learn the fundamentals of Siam Mask, as well as how to use it to segment and track online objects quickly. As you learn about the origins of Siam Mask, how it was developed, and how it was performed in real-world tests, you will be able to see for yourself how well it performed. In our next step, we will conduct a paper review to get a better understanding of the architecture of Siamese networks in the context of computer vision.
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After that, our focus will be on the implementation of Siam Mask by establishing the environment for development so you can run it on your own PC. You will be taught how to train Siam Mask to handle your own custom applications once that is working.
In order to apply your new model in the real world, you will need to test it in a way that can be applied to real-life data.
Why Should I Take this Course?
This Siam Mask Object Tracking and Segmentation in OpenCV Python course is appropriate for you, as Siam Mask is a modeling tool that is accurate and highly capable, able to be applied to a wide variety of applications.
Who this course is for:
- Computer Vision Developers
- Python and OpenCV curious about Object Tracking
- Automated Data Annotation
Created by Augmented Startups
Last updated 6/2021
Size: 575.2 MB