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Human Activity Recognition Through Ensemble Learning of Multiple Convolutional Neural Networks
Human Activity Recognition Through Ensemble Learning of Multiple Convolutional Neural Networks
Human Activity Recognition Through Ensemble Learning of Multiple Convolutional Neural Networks

Human Activity Recognition Through Ensemble Learning of Multiple Convolutional Neural Networks

₹5,500.00
Product Code: Python - Machine Learning
Availability: In Stock
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Product Description

Aim:


               This paper aim to classify the different types of videos using deep learning framework with convolution neural network (CNN)


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. Deep learning plays a vital role in image processing. We use Convolutional neural network algorithms for 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.


Proposed System:


            In this proposed system, we propose the convolution neural network method for action recognition in video. The input video will be captured by using the webcam. The input video is converted into number of frames. Then the CNN (Convolution Neural Network) algorithm is used in order to detect the particular part of the frame. Then the maximum weight values are taken from the feature extraction frames by using the Convolution neural network. Finally the action will be detected in the videos and then the label (action name) is identified. Then that output taken to the firebase and the firebase value given to the user via android notification.


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  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
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  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
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  12. Full Project Documentation
  13. Online support


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  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

 

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  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.