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.
