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From: tguclu Guclu on 12 Jan 2010 04:52
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Hi I have an application which decides whether a human is handwaving,running or walking. The idea is i have segmented an action,say handwave,to its poses. Let's say

Example; for human1:pose7-pose3-pose7-..... represents handwave for human3:pose1-pose7-pose1-..... represents handwave for human7:pose1-pose1-pose7-..... represents handwave for human20:pose3-pose7-pose7-..... represents handwave

for human1 pose11-pose33-pose77-..... represents walking for human2 pose31-pose33-pose77-..... represents walking for human3 pose11-pose77-pose77-..... represents walking for human20 pose11-pose33-pose11-..... represents walking

and i used above vectors for training SVM and Neural Net in Matlab..

Now I test with it test images. Again I have segmented poses for test images.

For the vector sizes of test and train sets in MATLAB; SVM and Neural Net requires same vector sizes To make it work; If I append 0(assume it like pose0-which is an invalide pose) , to make sizes equal I have really good performance If I copy initial poses at the beginning and append them to the end until sizes are equal performance decreases.

For example; train set:pose1-pose2-pose4-pose7-pose2-pose4-pose7 (1st method)test set:pose3-pose1-pose4-0-0-0-0 or (2nd method)test set:pose3-pose1-pose4-pose3-pose1-pose4-pose3

I would expect to have better classification with 2nd method since appended values are actual values for poses. But pose0 is not a real pose.

Do you have any idea ? Regards
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