Real-time Hand Tracking using Modificated Flocks of Features Algorithm

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 mouse and keyboard. To achieve this we need to be able to efficiently track human hand in real-time. We focused on Flocks of Features algorithm introduced by Mathias Kölsch and Matthew Turk, which can track human hand continuously during various movements and pose variations. It uses Lucas-Kanade tracker for features located on a human hand. This algorithm can handle tracking of fast movements of non-rigid highly articulated objects such as hands. We propose modifications to this algorithm which mostly correspond to the preprocessing of the input frame by using histogram back projection of the skin color. This modification according to our testing provides more reliable feature tracking which results in better hand tracking efficiency.