Let me explain my need before I explain the problem.
I am looking forward for a hand controlled application.
Navigation using palm and clicks using grab/fist.
Currently, I am working with Openni, which sounds promising and has few examples which turned out to be useful in my case, as it had inbuild hand tracker in samples. which serves my purpose for time being.
What I want to ask is,
1) what would be the best approach to have a fist/grab detector ?
I trained and used Adaboost fist classifiers on extracted RGB data, which was pretty good, but, it has too many false detections to move forward.
So, here I frame two more questions
2) Is there any other good library which is capable of achieving my needs using depth data ?
3)Can we train our own hand gestures, especially using fingers, as some paper was referring to HMM, if yes, how do we proceed with a library like OpenNI ?
Yeah, I tried with the middle ware libraries in OpenNI like, the grab detector, but, they wont serve my purpose, as its neither opensource nor matches my need.
Apart from what I asked, if there is something which you think, that could help me will be accepted as a good suggestion.
You don't need to train your first algorithm since it will complicate things.
Don't use color either since it's unreliable (mixes with background and changes unpredictably depending on lighting and viewpoint)
Assuming that your hand is a closest object you can simply
segment it out by depth threshold. You can set threshold manually, use a closest region of depth histogram, or perform connected component on a depth map to break it on meaningful parts first (and then select your object based not only on its depth but also using its dimensions, motion, user input, etc). Here is the output of a connected components method:
Apply convex defects from opencv library to find fingers;
Track fingers rather than rediscover them in 3D.This will increase stability. I successfully implemented such finger detection about 3 years ago.
Read my paper :) http://robau.files.wordpress.com/2010/06/final_report_00012.pdf
I have done research on gesture recognition for hands, and evaluated several approaches that are robust to scale, rotation etc. You have depth information which is very valuable, as the hardest problem for me was to actually segment the hand out of the image.
My most successful approach is to trail the contour of the hand and for each point on the contour, take the distance to the centroid of the hand. This gives a set of points that can be used as input for many training algorithms.
I use the image moments of the segmented hand to determine its rotation, so there is a good starting point on the hands contour. It is very easy to determine a fist, stretched out hand and the number of extended fingers.
Note that while it works fine, your arm tends to get tired from pointing into the air.
It seems that you are unaware of the Point Cloud Library (PCL). It is an open-source library dedicated to the processing of point clouds and RGB-D data, which is based on OpenNI for the low-level operations and which provides a lot of high-level algorithm, for instance to perform registration, segmentation and also recognition.
A very interesting algorithm for shape/object recognition in general is called implicit shape model. In order to detect a global object (such as a car, or an open hand), the idea is first to detect possible parts of it (e.g. wheels, trunk, etc, or fingers, palm, wrist etc) using a local feature detector, and then to infer the position of the global object by considering the density and the relative position of its parts. For instance, if I can detect five fingers, a palm and a wrist in a given neighborhood, there's a good chance that I am in fact looking at a hand, however, if I only detect one finger and a wrist somewhere, it could be a pair of false detections. The academic research article on this implicit shape model algorithm can be found here.
In PCL, there is a couple of tutorials dedicated to the topic of shape recognition, and luckily, one of them covers the implicit shape model, which has been implemented in PCL. I never tested this implementation, but from what I could read in the tutorial, you can specify your own point clouds for the training of the classifier.
That being said, you did not mentioned it explicitly in your question, but since your goal is to program a hand-controlled application, you might in fact be interested in a real-time shape detection algorithm. You would have to test the speed of the implicit shape model provided in PCL, but I think this approach is better suited to offline shape recognition.
If you do need real-time shape recognition, I think you should first use a hand/arm tracking algorithm (which are usually faster than full detection) in order to know where to look in the images, instead of trying to perform a full shape detection at each frame of your RGB-D stream. You could for instance track the hand location by segmenting the depthmap (e.g. using an appropriate threshold on the depth) and then detecting the extermities.
Then, once you approximately know where the hand is, it should be easier to decide whether the hand is making one gesture relevant to your application. I am not sure what you exactly mean by fist/grab gestures, but I suggest that you define and use some app-controlling gestures which are easy and quick to distinguish from one another.
Hope this helps.
The fast answer is: Yes, you can train your own gesture detector using depth data. It is really easy, but it depends on the type of the gesture.
Suppose you want to detect a hand movement:
Detect the hand position (x,y,x). Using OpenNi is straighforward as you have one node for the hand
Execute the gesture and collect ALL the positions of the hand during the gesture.
With the list of positions train a HMM. For example you can use Matlab, C, or Python.
For your own gestures, you can test the model and detect the gestures.
Here you can find a nice tutorial and code (in Matlab). The code (test.m is pretty easy to follow). Here is an snipet:
%Load collected data
training = get_xyz_data('data/train',train_gesture);
testing = get_xyz_data('data/test',test_gesture);
%Get clusters
[centroids N] = get_point_centroids(training,N,D);
ATrainBinned = get_point_clusters(training,centroids,D);
ATestBinned = get_point_clusters(testing,centroids,D);
% Set priors:
pP = prior_transition_matrix(M,LR);
% Train the model:
cyc = 50;
[E,P,Pi,LL] = dhmm_numeric(ATrainBinned,pP,[1:N]',M,cyc,.00001);
Dealing with fingers is pretty much the same, but instead of detecting the hand you need to detect de fingers. As Kinect doesn't have finger points, you need to use a specific code to detect them (using segmentation or contour tracking). Some examples using OpenCV can be found here and here, but the most promising one is the ROS library that have a finger node (see example here).
If you only need the detection of a fist/grab state, you should give microsoft a chance. Microsoft.Kinect.Toolkit.Interaction contains methods and events that detects the grip / grip release state of a hand. Take a look at the HandEventType of InteractionHandPointer . That works quite good for the fist/grab detection, but does not detect or report the position of individual fingers.
The next kinect (kinect one) detects 3 joint per hand (Wrist, Hand, Thumb) and has 3 hand based gestures: open, closed (grip/fist) and lasso (pointer). If that is enough for you, you should consider the microsoft libraries.
1) If there are a lot of false detections, you could try to extend the negative sample set of the classifier, and train it again. The extended negative image set should contain such images, where the fist was false detected. Maybe this will help to create a better classifier.
I've had quite a bit of succes with the middleware library as provided by http://www.threegear.com/. They provide several gestures (including grabbing, pinching and pointing) and 6 DOF handtracking.
You might be interested in this paper & open-source code:
Robust Articulated-ICP for Real-Time Hand Tracking
Code: https://github.com/OpenGP/htrack
Screenshot: http://lgg.epfl.ch/img/codedata/htrack_icp.png
YouTube Video: https://youtu.be/rm3YnClSmIQ
Paper PDF: http://infoscience.epfl.ch/record/206951/files/htrack.pdf
I am looking for camera calibration techniques with OpenCV and saw the chessboard and circles methods, but I wanted to calibrate the camera with something that is in the real world and you don't have to print (printers are also not very accurate in what they print).
Is it possible to do calibration with complex shapes like the Coca Cola logo on the cans? Is it a problem that the surface is curved?
Thanks
Depending on what you want to achieve this is not at all necessarily a bad idea, and you are not the first one who had it. There was a technology that uses a CD, which is a strongly standardised object which at least used to exist on most households, for a simple camera calibration task. (There is little technical to be found online about this, as the technology was proprietary. This is business document, where the use of the CD is mentioned. Algorithmically, however, it is not difficult if you know camera calibration.)
The question is whether the precision you get is sufficient for your application. Don't expect any miracles here. Generally you can use almost any object you like to learn something about a camera, as long as you can detect it reliably and you know its geometry. Almost certainly you will have to take several pictures of the object. Curved surfaces are no problem per see. I regularly used a cylinder (larger than a beverage can, though, with a simple to detect pattern) to calibrate a complete camera rig of 12 SLRs.
Don't expect to find out of the box solutions and don't expect implementation to be trivial. You will have to work your way through the math. I recommend the book by Hartley and Zisserman, Multiple View Geometry for Computer vision. This paper describes an analysis-by-synthesis approach to calibration, which is the way to go for here (it does not describe exactly what you want, but the approach should generalise to arbitrary objects as long as you can detect them).
i can understand your wish, but it's a bad idea.
the calibration algorithm works by comparing real world points from the cam with a synthetical model ( yes, you have to supply that , too! ). so, while it's easy to calculate a 2d chessboard grid on the fly and use that, it will be very hard to do for your tin can, or any arbitrary household item you grab.
just give in, and print a rectangular chessbord grid to a piece of paper
(opencv comes with a pdf for that already).
don't use a real-life chessboard, a quadratic one is ambiguous to 90° rotation.
interesting idea.
What about displaying a checkerboard pattern (or sth else) on an lcd screen display and use that display as calibration pattern?? You would have to know the displaying size of the pattern though.
Googling I found this paper:
CAMERA CALIBRATION BASED ON LIQUID CRYSTAL DISPLAY (LCD)
ZHAN Zongqian
http://www.isprs.org/proceedings/XXXVII/congress/3b_pdf/04.pdf
comment: this doesn't answer the question about the coca-cola can but gives and idea for a solution to the grounding problem: camera calibration with a common object.
I am trying to detect and track hand in real time using opencv. I thought haar cascade classifiers would yield a fair result. After training with 10k and 20k positive and negative images respectively, I obtained a classifier xml file. Unfortunately, it detects hand only in certain positions, proving that it works best only for rigid objects. So I am now thinking of adopting another algorithm that can track hand, once detected through haar classifier.
My question is,if I make sure that haar classifier detects hand in a certain frame, certain position, what method would yield robust tracking of hand further?
I searched web a bit, and have understood I can go for optical flow of the detected hand , or kalman filter or particle filter, but also have come across their own disadvantages.
also, If I incorporate stereo vision, would it help me, as I can possibly reconstruct hand in 3d.
You concluded rightly about Haar features - they aren't that useful when it comes to non-rigid objects.
Take a look at the following papers which use skin colour to detect hands.
Interaction between hands and wearable cameras
Markerless inspection of augmented reality objects
and this paper that uses KLT features to track the hand after the first detection:
Fast 2D hand tracking with flocks of features and multi-cue integration
I would say that a stereo camera will not help your cause much, as 3D reconstruction of non-rigid objects isn't straightforward and would require a whole lot of innovation and development. However, you can take a look at the papers in the hand pose estimation section of this page if you wish to pursue 3D tracking.
EDIT: Also take a look at this recent paper, which seems to get good results.
Zhang et al.'s Real-time Compressive Tracking does a reasonable job of tracking an object, once it has been detected by some other method, provided that the motion is not too fast. They have an OpenCV implementation (but it would need a bit of work to reuse).
This research paper describes a method to track hands, without using gloves by using a stereo camera setup.
there have been similar questions on stack overflow...
have a look at my answer and that of others: https://stackoverflow.com/a/17375647/1463143
you can for certain get better results by avoiding haar training and detection for deformable entities.
CamShift algorithm is generally fast and accurate, if you want to track the hand as a single entity. OpenCV documentation contains a good, easy-to-understand demo program that you can easily modify.
If you need to track fingers etc., however, further modeling will be needed.
I'm planning to implement an application with augmented reality features. For one of the features I need an egomotion estimation. Only the camera is moving, in a space with fixed objects (nothing or only small parts will be moving, so that they might be ignored).
So I searched and read a lot and stumbled upon OpenCV. Wikipedia explicitly states that it could be used for egomotion. But I cannot find any documentation about it.
Do I need to implement the egomotion algorithm by myself with OpenCV's object detection methods? (I think it would be very complex, because objects will move in different speed depending on their distance to the camera. And I also need to regard rotations.)
If so, where should I start? Is there a good code example for a Kanade–Lucas–Tomasi feature tracker with support for scaling and rotation?
P.S.: I also know about marker based frameworks like vuforia, but using a marker is something I would like to prevent, as it restricts the possible view points.
Update 2013-01-08: I learned that Egomotion Estimation is better known as Visual Odometry. So I updated the title.
You can find a good implementation of monocular visual odometry based on optical flow here.
It's coded using emgucv (C# opencv wrapper) but you will find no issues on convert it back to pure opencv.
Egomotion (or visual odometry) is usually based on optical flow, and OpenCv has some motion analysis and object tracking functions for computing optical flow (in conjunction with a feature detector like cvGoodFeaturesToTrack()).
This example might be of use.
Not a complete solution, but might at least get you going in the right direction.
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