human detection in a room

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Mahmoud Al-Faris
Mahmoud Al-Faris 2016 年 2 月 10 日
コメント済み: Walter Roberson 2016 年 2 月 14 日
Dear all,
I am trying to detect human inside a room and I 've tried HOG, Background subtraction, and Viola-Jones.
now, the problem is, I am trying to detect the human continuously as well as he still in the room even if he changes his pose or still constant without any move.
the above techniques depend on the specific status such upright for HOG, motion for background subtraction, and front or back view for Viola-Jones.
any suggestion please?
thanks a lot
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Walter Roberson
Walter Roberson 2016 年 2 月 11 日

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回答 (3 件)

Image Analyst
Image Analyst 2016 年 2 月 10 日
Try taking a shot of an empty room, then subtracting the images. Differences will show up but whether the difference is a human or a dog or a clock or simply a change in illumination level requires further steps.
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Walter Roberson
Walter Roberson 2016 年 2 月 12 日
If you can train against an image of the room with no human, then you can detect changes relative to that background and it is not required that you have the detector "learn" to ignore new items that do not move for a time. You still have the problem of detecting whether the non-background item is a human or not. As I remarked to one poster who dismissed that problem, "So you are designing your system to fail at science fiction conventions."

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Anand
Anand 2016 年 2 月 10 日
Can you say more about what you've tried - what worked and what didn't work?
It looks like you need to try a combination of vision.PeopleDetector, vision.CascadeObjectDetector and vision.ForegroundDetector (background subtraction).
For example, if the person is not moving, they can still be detected with the vision.PeopleDetector, even if the vision.ForegroundDetector misses them.
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Mahmoud Al-Faris
Mahmoud Al-Faris 2016 年 2 月 12 日
Thanks Anand,
can I ask you about your previous answer, did you mean the combination of features for each method? for example vision.peopledetector and cascade.objectdetection.
many thanks

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Walter Roberson
Walter Roberson 2016 年 2 月 11 日
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Walter Roberson
Walter Roberson 2016 年 2 月 14 日
If you can have different lighting conditions then you will need to train on one picture of the room for each major difference in lighting conditions. For example if you were to train only with overhead lights on then if you were to present a picture of the same room in sunlight then the system could end up detecting the sunlight as a "new" object.
With the lighting accounted for, you can use background subtraction to detect objects that were not previously present. You cannot, however, detect if the objects are human or not. For example if someone brought in a bag of groceries, your system would be able to detect that but not whether the bag was a human or not. (Can you really tell from the center picture above that the camouflaged object is in fact a human and not a doll or a bit of paper and a pair of shoes?) As soon as you say "Well, the system should learn to ignore the bag of groceries because it doesn't move", then you are telling the system to learn to ignore a human who does not move.

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