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| Mike McCracken
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10-23-2003 02:33 AM ET (US)
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The fact that you need to have previous data to match with doesn't seem like such a weakness to me. I expect that in most applications, previous data wouldn't be too hard to come by, and something as simple as flagging "unclassifiable action" for human review would be good enough to cover cases where the subjects being monitored began to levitate, for instance...
The discussion about how the examples all have static backgrounds reminded me of the Three Brown Mice project (from Kristin and Serge) - I was thinking that if a football player were running next to another player, the scheme from the paper would break down. However, in the mice project, they were able to classify actions despite occlusions (if I recall correctly) It seems like you'd have to do more for this to work with different backgrounds, and some backgrounds might just be too hard..
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| Sunny Chow
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10-23-2003 03:13 AM ET (US)
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Mike,
You're right, I misspoke a bit when I said it was a weakness..
One of the algorithm's applications was to remove occlusion (which suggests it will handle limited occlusion okay), but I doubt it will be able to handle much more than a simple head bobbing into view.
As for backgrounds, as long as the background is not moving relative to the subject, I think all will be good. However, if the subject was running around with a busy background, the motion induced by the background may throw off the classification.
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| Shinko Cheng
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10-30-2003 10:16 PM ET (US)
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Edited by author 10-30-2003 10:39 PM
Optical flow is relatively appearance invariant. The implications of using it to recognize certain motions is very interesting. I hope there can be some extensive flow models of various human motions, creating a library with which to perform recognition tasks.
The work reminds me of the randomized looping of video sequences, e.g. taking a sequence of a candle flame and looping it over and over again at random places. In it, they also used the similarity matrix but on full image frames.
This paper claims that the novel contribution is the motion descriptor. There appears to be however so much other work on the side for it. It appears to me that this person spent a lot of time on presenting its promise: skelton transfer, action synthesis, figure correction, "action database", if these things were indeed not research contributions themselves as the author seems to imply. The action synthesis one certainly doesn't sound too new; candle flame looping uses a similar concept confusion matrix concept. I guess what i'm saying is a follow up paper is called for on all the "promise" made in the paper.
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| Omer Rashid
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05-20-2004 07:18 PM ET (US)
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hello All, hope that u all are fine ...Recognizing at a distance . I am doing my final project in the same descipline . Its recognition of human through gaits. Can any one suggest me a good techique for that . I am implementing . I am currently working on the backgroud removal and stuff like that and then I have planned to work on the object by taking the stick image of the object after removing the background. I want u all to suggest me some thing good for that to recognize or the distinguishing features of human in gaits which distinguishes them from other . I shall be very thankful to u . Bye . Omer
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mcity4
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09-08-2004 12:44 AM ET (US)
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| mcity4
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12
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11-29-2004 11:25 PM ET (US)
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Messages 13-16 deleted by topic administrator between 05-16-2008 08:08 AM and 07-23-2006 02:04 AM |
| cila
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06-12-2008 04:16 AM ET (US)
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| sunglow
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06-13-2008 05:15 AM ET (US)
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Messages 19-20 deleted by topic administrator between 07-21-2008 02:09 AM and 06-25-2008 02:26 AM |
| travestia
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07-21-2008 01:30 AM ET (US)
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| travestia
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07-24-2008 03:42 AM ET (US)
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