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Siam Mask Object Tracking and Segmentation in OpenCV Python

Siam Mask Object Tracking and Segmentation in OpenCV Python

Implement Real-Time Object Tracking and Segmentation using OpenCV Python

What you’ll learn

Siam Mask Object Tracking and Segmentation in OpenCV Python

  • 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


  • Python Programming Experience
  • PC or Laptop
  • Nvidia CUDA enabled – GPU (Optional)
  • OpenCV Experience


What Is Siam Mask

In this course, you will learn how to implement both real-time object tracking and semi-supervised video object segmentation with a single simple approach. Siamak improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting the loss with a binary segmentation task.

Once trained, SiamMask solely relies 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.

Despite its simplicity, versatility, and fast speed, our strategy allows us to establish a new state-of-the-art among real-time trackers on the VOT-2018 dataset, while at the same time demonstrating competitive performance and the best speed for the semi-supervised video object segmentation task on DAVIS-2016 and DAVIS-2017

Applications of Siam Mask

  • Automatic Data Annotation – Regardless of Class
  • Rotoscoping
  • Robotics
  • Object Detection and targeting
  • Virtual Background without Green Screen

What you will Learn?

You will learn the fundamentals of Siam Mask and how it can be used for fast online object tracking and segmentation. You will first learn about the origins of Siam Mask, how it was developed as well its amazing performance on real-world tests. Next, we do a paper review to understand more about the architecture of Siamese Networks with regards to computer vision.

Thereafter, we move on to the implementation of Siam Mask by setting up the environment for development so that you can run Siam Mask on your own PC or Laptop. Once that is working, we will show you how to train Siam Mask for your own custom applications.

Once trained, you will need a method in which to test your new model so that you can apply it for real-world applications.

Why Should I Take this Course?

You should take this course because Siam Mask is a State of Art Model that has robust accuracy and performance and can be used in a wide variety of applications.

Who this course is for:

  • Computer Vision Developers
  • Python and OpenCV curious about Object Tracking
  • Automated Data Annotation
  • Last updated 6/2021

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