How to extract features of videos

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Kong
Kong 2020-3-23
评论： Image Analyst 2020-3-29
Hello.
I want to classify videos after extracting features.
When I use background substraction as extracting features, the result of classification is not good.
Could I get some ideas about feature extraction in videos?
This is code about background substraction. I want to use other methods for feature extraction
%// read the video:
list = dir('*.avi')
% loop through the filenames in the list
for k = 1:length(list)
reader = VideoReader(list(k).name);
vid = {};
while hasFrame(reader)
vid{end+1} = im2single(readFrame(reader));
end
%// simple background estimation using mean:
bg = csvread('background.csv');
bg = reshape(bg, 144, 180, 3);
%// estimate foreground as deviation from estimated background:
for i=1:25
fIdx(i) = i; %// do it for frame 1 ~ 60
fg{i} = sum( abs( vid{fIdx(i)} - bg ), 3 );
fgh{i} = imresize(fg{i}, 0.2);
fg{i} = reshape(fgh{i},[],1);
end
X = cell2mat(fg);
  1 个评论
Kenta
Kenta 2020-3-29
Another option is the use of pre-trained convolutional neural network.
For example, a video can be classified with CNN and LSTM.
https://jp.mathworks.com/matlabcentral/fileexchange/74402-video-classification-using-lstm-lstm

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Image Analyst
Image Analyst 2020-3-23
I extract some features in the attached demo. I compute a dynamic background from the last few frames and subtract it to get the changes, and I also plot the mean R, G, and G values. You could of course compute other things -- whatever you want. If background subtraction is not good for what you want to segment, then you'll have to devise a different segmentation routine. Background subtraction is not appropriate for all situations obviously.
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Kong
Kong 2020-3-24
Yes. I have Computer Vision Toolbox.
When I use the foreground detector, the result is not good like this.
Could you give an idea to solve this?
clear all
close all
%// read the video:
reader = VideoReader('shahar_bend.avi');
vid = {};
while hasFrame(reader)
vid{end+1} = im2single(readFrame(reader));
end
%// simple background estimation using mean:
bg = mean( cat(4, vid{:}), 4);
%// estimate foreground as deviation from estimated background:
fIdx = 40; %// do it for frame 43
fg1 = sum( abs( vid{fIdx} - bg ), 3 ) > 0.25;
fg2 = imresize(fg1, 0.5);
figure;
subplot(141); imshow( bg );
subplot(142); imshow( vid{fIdx} );
subplot(143); imshow( fg2 );
Image Analyst
Image Analyst 2020-3-29
What I would do it to use a higher resolution camera and use less compression, because the images are of horribly low quality - almost unusable.

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