Integration of gesture and posture recognition systems for interpreting dynamic meanings using particle filter

Abstract

This paper proposes a novel approach for determining the integration criteria using Particle filter for fusion of hand gesture and posture recognition system at decision level. For decision level fusion, integration framework requires the classification of hand gesture and posture symbols in which HMM and SVM are used to classify the alphabets and numbers from gesture and posture recognition system respectively. These classification results are input to integration framework to compute the contribution-weights. For this purpose, Condensation algorithm is employed to approximate optimal a-posterior probability using a-prior probability and Gaussian based likelihood function thus making the weights independent of classification ambiguities. Considering the recognition as a problem of regular grammar, we have developed the production rules based on Context Free Grammar for restaurant scenario. On the basis of contribution-weights, we mapped the recognized outcome over CFG rules and infer meaningful expressions. Experiments are conducted on 500 different combinations of restaurant orders with overall 98.3% inference accuracy which proves the significance of proposed approach.