Deep learning video exercise classification

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Utpal Das
Utpal Das on 27 Sep 2022
Commented: Utpal Das on 18 Oct 2022
Sir, it is possible by using Matlab's deep learning model. We train through our desired video and later test it with another video. Please guide me on how it is possible.
  6 Comments
Christopher Stapels
Christopher Stapels on 18 Oct 2022
Ok thanks, I rremoved the ThingSpeak tag to help other users. Good luck with your project.
Utpal Das
Utpal Das on 18 Oct 2022
Thank you for telling me about Thingspeak. Is there any possibilities to do with Thingspeak

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Answers (1)

Rahul
Rahul on 12 Oct 2022
There are variety of application you can do with deep learning. As per your question, the outcome that you want to achieve is not clear. I am assuming you want to classify by giving the video input as training. In supervised classification, I assume you have labeled video that you can use for training. You can extract the frames of a recorded video and then based on the extracted images, you can train your CNN model. Below is the code for how to extract frames from a recorded video file. The variable "all_frames" contains alll the frames present in a video. Use these frames to train your CNN model. The related documentation links are given below:
Once the training is complete, you can use the same code to extract images of a test video and predict your outcome using a trained model. Hope this helps.
P.S.: Use MATLAB R2016a or later.
vidObj = VideoReader('xylophone.mp4');
if contains(vidObj.VideoFormat, 'RGB')
% This is created if the video is of format RGB.
all_frames = zeros( vidObj.Height, vidObj.Width, 3, vidObj.NumFrames, 'uint8' );
else
% This is created if the video is of format grayscale.
all_frames = zeros( vidObj.Height, vidObj.Width, vidObj.NumFrames, 'uint8' );
end
ii = 1;
while( hasFrame( vidObj ) )
frame = readFrame( vidObj );
if length( size( all_frames ) ) == 4
all_frames(:, :, :, ii) = frame;
else
all_frames(:, :, ii) = frame;
end
ii = ii + 1;
end
imshow( all_frames( :, :, :, 1 ) ) % displaying 1st frame

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