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human activity recognition in image

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I am trying to implement a paper of human activity recognition, I am just started after specified the steps of the system. I found two points that I did not understand
1- calculate the normalized bounding box coordinate?!
2- If I going to use background subtraction to get silhouette, how can I get silhouette features, I found that there are many types of local features (blob, corner, region of interest) what the difference among these
Thanks a lot


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Accepted Answer

Walter Roberson
Walter Roberson on 6 Mar 2016
regionprops() to get the bounding box. Each region will have a bounding box that is a row vector of 4 items. Divide the first and third members by the number of columns (not rows!) in the image, and divide the second and fourth members by the number of rows in the image.
[rows, cols, panes] = size(YourImage);
props = regionprops(BWimage, 'BoundingBox');
bbox = vertcat(props.BoundingBox); %put all blob's boxes together
nbbox = [bbox(:,1)./cols, bbox(:,2)./rows, bbox(:,3)./cols, bbox(:,4)./rows];
Now nbbox will be normalized bounding box in the form [left_edge, bottom_edge, width, height] which can easily be converted to right edge and top edge instead of width and height.


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Mammo Image
Mammo Image on 7 Mar 2016
what is the benefit of the bounding box normalization, since I found in this paper that he did the normalization before motion detection.
Walter Roberson
Walter Roberson on 7 Mar 2016
If you had two different images of a scene taken at different resolutions, then the portion of the scene occupied by a particular object would be the same, but the number of pixels would be different. Working in portions allows you to avoid worrying about resolution.

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