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Deep Ensemble Machine for Video Classification
Deep Ensemble Machine for Video Classification
Deep Ensemble Machine for Video Classification

Deep Ensemble Machine for Video Classification

₹4,000.00
Product Code: Matlab - Deep Learning
Availability: In Stock
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Product Description

Abstract:

        Video classification has been extensively researched in computer vision due to its wide spread use in many important applications such as human action recognition and dynamic scene classification. It is highly desired to have an end-to-end learning framework that can establish effective video representations while simultaneously conducting efficient video classification.

        The convolution 3-D (C3-D) and VGG (vision and graphics group) are first deployed to extract temporal and spatial features from the input videos cooperatively, which establishes comprehensive and informative representations of videos. VGG and C3-D are chosen due to their strong capability of extracting complementary spatial and temporal features for comprehensive video representations. The introduced RLE layer is further deployed to encode the initial outputs of classifiers, which is followed by a weighting layer jointly learned in the end-to-end framework to combine classification results. In this proposed system, we propose the Deep belief network method for action recognition in video.

        The input video is converted into number of frames. The converted frames will be stored in the database. Then the ROI (Region of interest) segmentation algorithm is used in order to detect the particular part of the frame. Finally the deep belief network classifier is used to detect the given input video is abnormal or not.


Proposed System:

        In this proposed system, we propose the Deep belief network method for action recognition in video. The input video is converted into number of frames. The converted frames will be stored in the database. The frame is feed into the feature extraction (in order to extract the feature from the video frames). Then the ROI (Region of interest) segmentation algorithm is used in order to detect the particular part of the frame. Finally the deep belief network classifier is used to detect the given input video is abnormal or not.



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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.