Multiple hypothesis tracking with sign language hand motion constraints

Abstract

In this paper, we propose to incorporate prior knowledge

from sign language linguistic models about the motion of the hands

within a multiple hypothesis tracking framework. A critical component

for automated visual sign language recognition is the tracking of the

signer’s hands, especially when faced with frequent and persistent occlu- sions and complex hand interactions. Hand motion constraints identified

by sign language phonological models, such as the hand symmetry condi- tion, are used as part of the data association process. Initial experimental

results show the validity of the proposed approach.