intend to programme navigating an iPad App with head motions (originally hand motions, but hands seem to difficult to detect at the moment): left, right, up and down. So I plan to use openCV and either detect the optical flow of the head area, or detect the ears and head of the user with haar cascades (openCV is delivered with quite precise head and ear xmls). Can anyone offer some advice on which solution to use? Will one of the mentioned need more power? Ear and head detection might be easier to programme? I would like to avoid to much effort in the wrong direction and don't have much expertise in my current surroundings...
thank you for any help, advice, ideas!
I would suggest to use Haar cascades because optical flow is more expensive in computing time!
Is using the native face detection in iOS 5 not an option for what you want?
I'm thinking outside the box here, but I've been with OpenCV before and it still hurts..
http://maniacdev.com/2011/11/tutorial-easy-face-detection-with-core-image-in-ios-5/
*The idea of course, being that you apply this to live input somehow
One way could be using Hidden Markov Models. There is a lot of research material(working prototypes) on how to use HMMs to recognize head gestures.
I believe ear haar classifiers are not that effective with cluttered background. Please let us know if you've got it to work!
Related
with image processing libraries like opencv you can determine if there are faces recognized in an image or even check if those faces have a smile on it.
Would it be possible to somehow determine, if the person is looking directly into the camera? As it is hard even for the human eye to determine is someone is looking into the camera or to a close point, i think that this will be very tricky.
Can someone agree?
thanks
You can try using an eye detection program, I remember doing back a few years ago, and it wasn't that strong, so when we tilt our head slightly off the camera, or close our eyes, the eyes can't be detected.
Is it is not clear, what I really meant was our face must be facing straight at the camera with our eyes open before it can detect our eyes. You can try doing something similar with a bit of tweaks here and there.
Off the top of my head, split the image to different sections, for each ROI, there are different eye classifiers, for example, upper half of the image, u can train the a specific classifiers of how eyes look like when they look downwards, lower half of image, train classifiers of how eyes look like when they look upwards. and for the whole image, apply the normal eye detection in case the user move their head along while looking at the camera.
But of course, this will be based on extremely strong classifiers and ultra clear quality images, video due to when the eye is looking at. Making detection time, extremely slow even if my method is successful.
There maybe other ideas available too that u can explore. It's slightly tricky, but it not totally impossible. If openCV can't satisfy, openGL? so many libraries, etc available. I wish you best of luck!
I'm using optical flow as a real time obstacle detection and avoidance system for the visually impaired. I'm developing the application in c# and using Emgu Cv for image processing. I use the Lucas and Kanade method and I'm pretty satisfied with the speed of the algorithm. I am using monocular vision thus making it hard for me to compute the depth accurately to each of the features being tracked and to alert the user accordingly. I plan on using an ultrasonic sensor to help with the obstacle detection due to the fact that depth computation is hard with monocular camera. Any suggestions on how I could get an accurate estimation of depth using the camera alone?
You might want to check out this paper: A Robust Visual Odometry and Precipice Detection System Using Consumer-grade Monocular Vision. They usea nice trick for detecting as well obstacles as holes in the field of view.
Hate to give such a generic answer, but you'd be best off starting with a standard text on structure-from-motion to get an overview of techniques. A good one is Richard Szeliski's recent book available online (Chapter 7), and its references. After that, for your application you may want to look at recent work in SLAM - Oxford's Active Vision group have published some great work and Andrew Davison's group too.
more a comment on RobAu's answer below,
'structure from motion' might give better search results, than '3d from video'
Depth from one care will only work if you have movement of the camera. You could look into some 3d from video approaches. It is a very hard problem, especially when the objects in the field of view of the camera are moving as well.
I'm attempting to implement an easter egg in a mobile app I'm working on. These easter egg will be triggered when a logo is detected in the camera view. The logo I'm trying to detect is this one: .
I'm not quite sure what the best way to approach this is as I'm pretty new to computer vision. I'm currently finding horizontal edges using the Canny algorithm. I then find line segments using the probabilistic Hough transform. The output of this looks as follows (blue lines represent the line segments detected by the probabilistic Hough transform):
The next step I was going to take would be to look for a group of around 24 lines (fitting within a nearly square rectangle), each line would have to be approximately the same length. I'd use these two signals to indicate the potential presence of the logo. I realise that this is probably a very naive approach and would welcome suggestions as to how to better detect this logo in a more reliable manner?
Thanks
You may want to go with SIFT using Rob Hess' SIFT Library. It's using OpenCV and also pretty fast. I guess that easier than your current way of approaching the logo detection :)
Try also looking for SURF, which claims to be faster & robuster than SIFT. This Feature Detection tutorial will help you.
You may just want to use LogoGrab's technology. It's the best out there and offers all sorts of APIs (both mobile and HTTP). http://www.logograb.com/technologyteam/
I'm not quite sure if you would find such features in the logo to go with a SIFT/SURF approach. As an alternative you can try training a Haar-like feature classifier and use it for detecting the logo, just like opencv does for face detection.
You could also try the Tensorflow's object detection API here:
https://github.com/tensorflow/models/tree/master/research/object_detection
The good thing about this API is that it contains State-of-the-art models in Object Detection & Classification. These models that tensorflow provide are free to train and some of them promise quite astonishing results. I have already trained a model for the company I am working on, that does quite amazing job in LOGO detection from Images & Video Streams. You can check more about my work here:
https://github.com/kochlisGit/LogoLens
I am trying to identify static hand signs. Confused with the libraries and algorithms I can use for the project.
What need to it identify hand signs and convert in to text. I managed to get the hand contour.
Can you please tell me what is the best method to classify hand signs.
Is it haar classifier, adaboost classifier, convex hull, orientation histograms, SVM, shift algorithm, or any thing else.
And also pls give me some examples as well.
I tried opencv and emugcv both for image processing. what is best c++ or c# for a real time system.
Any help is highly appreciated.
Thanks
I have implemented a handtracking for web applications in my master deggree. Basically, you should follow those steps:
1 - Detect features of skin color in a Region of Interest. Basically, put a frame in the screen and ask for the user put the hand.
2 - You should have a implementation of a lucas kanade tracker method. Basically, this alghorithm will ensure that your features are not lost through the frames.
3 - Try get more features for each 3 frames interval.
The people use many approaches, so I cannot give a unique. You could make some research using Google Scholar and use the keywords "hand sign", "recognition" and "detection".
Maybe you find some code with the help of Google. An example, the HandVu: http://www.movesinstitute.org/~kolsch/HandVu/HandVu.html
The haar classifier (method of Viola-Jones) help to detect hand, not to recognize them.
Good luck in your research!
I have made the following with OpenCV. Algorithm:
Skin detection made in HSV
Thinning (if pixel has zero neighbor than set zero)
Thicking (if pixel has neighbor nonzero then set it nonzero)
See this Wikipedia page for the details of these.
You can find the best trained cascade to detect hand using OpenCV from the GitHub...
https://github.com/Aravindlivewire/Opencv/blob/master/haarcascade/aGest.xml
Good luck...
Can anyone point me to a robust motion detection sample/implementation? I know EMGU has a motion detection sample, but its not very good, even small changes in light will be falsely detected as motion. I don't need to track objects. I am looking for a way to detect motion in a video that will not be falsely triggered by changing light conditions.
Have a look at AForge. Everything you should need is there (though you'll need to spend some time putting it all together), and it has a robust community if you need specific help.
I concur with nizmahone. use Aforge:
Here is a link with soem motiond etection in C#:
http://www.codeproject.com/KB/audio-video/Motion_Detection.aspx