Improving of Gesture Recognition Using Multi-hypotheses Object Association
Gesture recognition plays an important role in Human Computer Interaction (HCI) but in most HCI systems, the user is limited to use only one hand or two hands under optimal conditions. Challenges are for instance non-homogeneous backgrounds, hand-hand or hand-face overlapping and brightness modifications. In this research, we have proposed a novel approach that solves the ambiguities occurred due to the hand overlapping robustly based on multi-hypotheses object association. This multi-hypotheses object association builds the basis for the tracking in which the hand trajectories are computed and this leads us to extract the features. The gesture recognition phase takes the extracted features and classifies them through Hidden Markov Model (HMM).
Keywordshand tracking multi hypotheses HCI gesture recognition
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