  Dynamic hand tracking and gesture recognition is a hard task since there are
many joints on the fingers and each joint owns many degrees of freedom.
Besides, object occlusion is also a thorny issue in finger tracking and posture
recognition. Therefore, we propose a robust and customized system for realtime
hand tracking and gesture recognition under occlusion environment. First, we
model the angles between hand keypoints and encode their relative coordinate
vectors, then we introduce GAN to generate raw discrete sequence dataset.
Secondly we propose a time series forecasting method in the prediction of
defined hand keypoint location. Finally, we define a sliding window matching
method to complete gesture recognition. We analyze 11 kinds of typical gestures
and show how to perform gesture recognition with the proposed method. Our work
can reach state of the art results and contribute to build a framework to
implement customized gesture recognition task.
