Recognizing gestural actions

In visual interaction environments, hands and arm provide a natural communication medium to interact with computers for recognizing meaningful actions. So, in this context, a novel approach is proposed to recognize the gestural actions by visual modality which comprises of four main modules. First, the dynamic contents in the scene are captured by optical flow and marginalized using Gaussian Mixture Model (GMM). Second, the resulting mixture of Gaussians are pruned by applying skin-based criterion to obtain the Gaussians containing both the skin and flow information. Third, the intra-level merging step is performed to obtain a concrete Gaussian representation spatially and then Kullback-Leibler (KL) divergence is used to obtain the inter-level linking among these Gaussians temporally. Fourth, the temporal features are computed from these linked Gaussians which are classified with Hidden Markov Model (HMM) to recognize the gestural actions. The experimental results show that our proposed approach is capable to recognize gestural actions in real situations which proves its applicability and usability in the domain of Human Computer Interactions (HCI).