Optical Flow-Guided Mask Generation Network for Video Segmentation
Objective
A CNN based method for the semi-supervised video object segmentation, where a hybrid encoder-decoder network is designed to generate pixel-wise foreground object segmentation in use of both spatial and temporal information.
Abstract
The main theme of this project is to generate an optical flow-guided mask for video/image segmentation of animals. The main purpose of the segmentation is to differ the foreground objects. In this project we propose CNN based technique for the semi-supervised video/image segmentation. Here we will train the data by using segmentation techniques for the segmentation of objects to get the ground truth values.
Image/video will be treated as input to Unet architecture and it will create a mask over the segmented objects. By the use of Unet, we can easily create the mask over foreground segmented objects. The generated mask cannot be seen by using the Unet, hence we use semantic segmentation to visualize the segmented output. By using the semantic segmentation, we can visualize the foreground objects from the video/image.
Keywords: Semi-supervised, Video Object Segmentation, Optical Flow, Mask.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software & Hardware Requirements:
Software Requirements:
MATLAB R2018a or above
Hardware Requirements:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support.
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended a full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
- Introduction to Matlab
- What is EISPACK & LINPACK
- How to start with MATLAB
- About Matlab language
- Matlab coding skills
- About tools & libraries
- Application Program Interface in Matlab
- About Matlab desktop
- How to use Matlab editor to create M-Files
- Features of Matlab
- Basics on Matlab
- What is an Image/pixel?
- About image formats
- Introduction to Image Processing
- How digital image is formed
- Importing the image via image acquisition tools
- Analyzing and manipulation of image.
- Phases of image processing:
- Acquisition
- Image enhancement
- Image restoration
- Color image processing
- Image compression
- Morphological processing
- Segmentation etc.,
- About Artificial Intelligence (AI)
- About Machine Learning
- About Deep Learning
- About layers in AI (input, hidden and output layers)
- Building AI (ANN/CNN) architecture using Matlab
- We will able to know, what’s the term “Training” means in Artificial Intelligence
- About requirements that can influence the AI training process:
- Data
- Training data
- Validation data
- Testing data
- Hardware requirements to train network
- How to select the features of object using AI
- How to extend our work to another real time applications
- Project development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
Related Projects
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
