Semantic Boundary Detection with Reinforcement Learning For Continuous Sign Language Recognition

Semantic Boundary Detection with Reinforcement Learning For Continuous Sign Language Recognition

₹5,000.00
Product Code: Matlab-Machine Learning
Availability: In Stock
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Product Description

                                    Technology: Machine Learning                  Tool: Matlab Matlab R2018a



Objective


              The main aim of this project is used to recognize the sign language verification by using deep learning techniques.


ABSTRACT


               In this paper, currently, there are tens of millions of people around communication way, sign language conveys semantic meaning by hand movement, gesture appearance, facial expression, etc. Image-based sign language recognition (SLR) is studied to automatically translate a sign image into ordered sign glosses, which may be further converted into natural language text or synthetic audio, to facilitate the communication between hearing-disabled people and hearing-normal people. With the huge potential social impact as well as the academic significance, video-based SLR has attracted increasing attention in the computer vision and multimedia fields. In this existing method is, a novel semantic boundary detection method based on reinforcement learning for accurate continues SLR.   


               Then, we formulate the semantic boundary detection as a reinforcement learning problem. In this paper, we propose a novel deep architecture for weakly supervised continuous SLR. Our framework consists of a weakly supervised learning component with a multi-scale perception module to learn discriminative image representation, and a reinforcement learning component to detect the semantic boundaries for further representation refinement, and enhance the performance.


PROPOSED METHOD


              Propose a stacked temporal convolution module to capture short-term temporal transition on local adjacent video clips for continuous SLR. For spatial-temporal feature extraction in continuous SLR, many works, adopt both 2D-CNN and temporal convolution in their networks, which effectively promotes the feature discrimination. There are two stages in our method. First, we proposed a multi scale perception strategy to facilitate image representation by weakly supervised learning (SL). Second, we learn an agent called SBD-RL to detect the semantic boundaries in videos by reinforcement learning (RL). The two stages are trained separately. The output of the first stage is used as the input of the second stage. In this section, we expound the two stages separately.


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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.