Real-time Hand Tracking using Flocks of Features

There is a growing demand to interact with computers in a more natural way. For example using hand gestures to interact with certain type of applications would be more efficient than old-fashioned keyboard and mouse. Hand tracking is one of the key problems in computer vision. We have analyzed many different approaches used for hand tracking. Flocks of features introduced by Mathias Kölsch and Matthew Turk can track human hand continuously during various movements and pose variations. It uses the Kanade Lucas Tomasi (KLT) tracker for features located on a human hand to track them in a frame sequence. It can handle fast tracking of non-rigid highly articulated objects such as hands. We propose an improvement to this algorithm by processing the frame using histogram back projection of the skin color prior to applying flocks of features (FoF). This modification provides better results with lower false positive error.