4. A method for tracking positions of parts of an object, comprising controlling a processor to: obtain a training depth image; extract a foreground depth image of an object comprising parts from the training depth image, the foreground depth image of the object comprising some of the pixels of the training depth image and corresponding depth information, extract patterns by storing differences between the depth information of each pixel of the foreground depth image of the object and the depth information of pixels surrounding the each pixel of the foreground depth image of the object as a vector, each of the surrounding pixels being in either the foreground depth image of the object or a portion of the training depth image outside the foreground depth image of the object, and each of the surrounding pixels being located at a predetermined distance in a radial direction from the each pixel, said predetermined distance being greater than 1 pixel; generate a database including information about a part of the object corresponding to each pixel from the foreground depth image of the object; extract features for each pixel of the foreground depth image of the object by calculating a histogram based on a frequency of the extracted patterns; generate a feature set for each of the parts of the of the object based on the extracted features; train a classifier to distinguish a position of each part of the object in the foreground depth image of the object by using the generated feature set for each part of the foreground depth image of the object; extract features of each pixel of an input image object of an input depth image in a state in which the classifier is trained; and track three-dimensional position information of each part of the input image object in the input depth image using the trained classifier and the extracted features of each pixel of the input image object.
4. A method for tracking positions of parts of an object, comprising controlling a processor to: obtain a training depth image; extract a foreground depth image of an object comprising parts from the training depth image, the foreground depth image of the object comprising some of the pixels of the training depth image and corresponding depth information, extract patterns by storing differences between the depth information of each pixel of the foreground depth image of the object and the depth information of pixels surrounding the each pixel of the foreground depth image of the object as a vector, each of the surrounding pixels being in either the foreground depth image of the object or a portion of the training depth image outside the foreground depth image of the object, and each of the surrounding pixels being located at a predetermined distance in a radial direction from the each pixel, said predetermined distance being greater than 1 pixel; generate a database including information about a part of the object corresponding to each pixel from the foreground depth image of the object; extract features for each pixel of the foreground depth image of the object by calculating a histogram based on a frequency of the extracted patterns; generate a feature set for each of the parts of the of the object based on the extracted features; train a classifier to distinguish a position of each part of the object in the foreground depth image of the object by using the generated feature set for each part of the foreground depth image of the object; extract features of each pixel of an input image object of an input depth image in a state in which the classifier is trained; and track three-dimensional position information of each part of the input image object in the input depth image using the trained classifier and the extracted features of each pixel of the input image object. 6. The method for tracking a position according to claim 4 , wherein the training of the classifier is performed using a randomized forests algorithm.