Improving of Gesture Recognition Using Multi-hypotheses Object Association
作者： Sebastian Handrich , Ayoub Al-Hamadi , Omer Rashid
DOI: 10.1007/978-3-642-31254-0_34
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摘要: Gesture recognition plays an important role in Human Computer Interaction (HCI) but 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 hand-face overlapping and brightness modifications. In this research, we have proposed a novel approach that solves ambiguities occurred due robustly based on multi-hypotheses object association. This association builds basis tracking which trajectories computed leads us extract features. The gesture phase takes extracted features classifies them through Hidden Markov Model (HMM).
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