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I am looking for open source Face/ Image Detection, Recognition, Video Face Mining libraries similar to the ones that were at one time available from Pittsburg Pattern Recognition. I am also interested in libraries that detect various states of facial expressions that would work on captured still images.libra I looked at OpenCV but I was not able to find a cohesive library. Also, I would be interested in getting in touch with any group that would do contract development or license available libraries. Much thanks in advance.
OpenCV has support for Face Detection and even gesture recognition, such as smile recognition, like this: auto-smiley.
openFrameworks is a good library which wraps OpenCV and makes life considerably easier, very sophisticated image processing can be done with the two.
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I am working on a project wherein I am supposed to detect human beings from a live video stream which I get from a UAV's camera module. I do not need to create any rectangles or boxes around detected subjects, but just need to reply with a yes or no. I am fairly new to Open-CV and have no prior experience.
What I have tried:
I started by training my SVM on HOG features. My team gathered a few images from a UAV we had, with people in it. I then trained the SVM from the crops of those people. We got unsatisfactory results when we used the trained detector on the a video from sky with people. Moreover processing each frame turned out to be very slow , therefore the system became unusable.(it did work on still images to some extent).
My question:
I wanted to know if there is some other technique, library etc I could try for achieving good results. Please point me to the next step.
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I am very beginner in robotics. I want to make a robotics project based on slam algorithms. I know many algorithm and i have the confidence to implement it in any language but i dont have any idea based on image processing and hardware. So, can anyone give a tuotorial based on slam based robotics projects[including how hardware organized and how image processing is done for that project], after seeing that i can make a slam based robotics project from my own.
In addition, If anyone give me a video lecture series for that then it would be very helpful.
Thanks in advance.
I have tried to do something similar last year. I created two systems. The first system made use of a camera and laser to detect objects and determine their location relative to the system itself. The second system was a little robot with tracks (wheels would be better), that used dead reckoning to keep track of its own location relative to its starting location. The techniques worked really well, but unfortunately I did not have the time to combine the two systems. I can however provide you with some documentation that was incredibly useful for me at that time.
These tutorials provide information on both the hardware and the software.
Optical Triangulation (detection of objects with a camera and laser) :
http://www.seattlerobotics.org/encoder/200110/vision.htm
Dead Reckoning (a technique to keep track of one's own location) :
http://www.seattlerobotics.org/encoder/200010/dead_reckoning_article.html
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I was curious if anyone knew of something like this flowchart but for Computer Vision tasks? Specifically for OpenCV would be most ideal.
Or any references with best practices, and common patterns for Computer Vision problems?
That's a monumental task. The best I could find is from this article and it's a little bit old:
Maybe it's a good time to commit to FlexCV on Kickstarter.com, a GUI for OpenCV that allows you to create complex algorithms in a matter of minutes by connecting graphical elements together. It's an alternative for Adaptive Vision, but purely based on OpenCV features.
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I have found OpenCV code that uses CvGaussBGStatModelParams structure, cvCreateGaussianBGModel, and other related functions. However, I haven't been able to find any explanations for how they work and how they are to be used and what they mean.
Any help would be appreciated.
These functions are undocumented (at least as far as the manual goes). However, if you look around in the source, you will find them in src/cvaux/cvbgfg_gaussmix.cpp. In there:
This is based on the "An Improved
Adaptive Background Mixture Model for
Real-time Tracking with Shadow
Detection" by P. KaewTraKulPong and R.
Bowden
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
The windowing method is used, but not
the shadow detection. I make some of
my own modifications which make more
sense. There are some errors in some
of their equations.
That link is probably a good start for you.
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Been tinkering with OpenCV in python. The face detection demo is impressive.
Are there any free collection of Haar classifiers aside from the face, eyes and full body ones?
Sure, there are a few common ones which show up in most people's projects, although opencv should come with these:
cv haar cascades
eye cascades
You can make your own cascades, but be warned, it's a pretty cumbersome process. The best tutorial I found was this one.
HTH
Alejandro F. Reimondo has a few cascades on his site: http://alereimondo.no-ip.org/OpenCV/34