how to solve occlusion problem in multiple object?

edit

Kalman Filter?

these few links might help you out:

http://stackoverflow.com/questions/2764238/image-processing-what-are-occlusions

http://vip.uwaterloo.ca/publications/vehicle-tracking-occlusion-and-clutter

http://web.engr.illinois.edu/~dhoiem/publications/iccv07hoiem.pdf

thanx Abhishek Kumar Annamraju. I got knowledge from this . Can you tell me how to use kalman filter in occlusion i found some reference code but don't know how to use it. how to relate kalman filter to occlusion.

@thdrksdfthmn Yeah Kalman filter. Which is actually use for occlusion.

But don't know how to use it.

and how to include on my own code

I think you should start with this for better understanding, then this for having an idea of code. The idea is to always enforce the prediction with a detection (and search for the object in a smaller area, around the prediction); if there is no detection for a few frames than you should loose the tracking (but this is when testing and tuning the filter)

@thdrksdfthmn thanx I will try and Let you know if I will Find Any Problem. Meanwhile Can you tell me how to train features of any object. And practical example for it.

Than in Advance. :)

I do not know what "to train features" means, but I know that features can be found using FeatureDetector. You shall also see the DescriptorExtractor that may be used for training.

@thdrksdfthmn Thanx for the reply. Train feature means...train own algorithm like left eye , right eye and all other face feature have own xml file. so i want specific shape base feature for that I have to train them and make xml file so that I can include directly in to my code . So do you know exact procedure to train feature.

yes, it is traincascade in opencv. These are the docs, here is a related question, so good luck!

@coco and @thdrksdfthmn Make sure you fetch all the other possibilities you have other than traincascade, try going up for advanced feature extractors like Weber Descriptors,SIFT, etc. Traincascade is a very tedious process and unless combined with parallel computing it is a very long job, even the results are uncertain as you need to play with the training parameters a lot. Look for SVM training also if you have to go up for machine learning. Anyway here's a tutorial from my blog for traincascade : http://abhishek4273.com/2014/03/16/traincascade-and-car-detection-using-opencv/