Non-temporal Mutliple Silhouettes in Hidden Markov Model for View Independent Posture Recognition

This paper introduces a non-temporal multiple silhouettes in Hidden Markov Model (HMM) for offering view independent human posture recognition. The multiple silhouettes are used to reduce the ambiguity problem of posture recognition. A simple feature extraction of the 2D shape contour based histogram is used for image encoding and K-Means algorithm is applied for clustering and code-wording of eight simple postures from multiple views. Therefore, 3D volume reconstruction is not required, in return helps to reduce the complexity of modeling and computational power of feature extraction. HMM is trained to obtain view independent recognition model using multiple silhouettes. A combination of non-temporal multiple silhouettes, code-wording and HMM methods in this proposed approach make it possible to recognize human posture in view independent. The experimental results demonstrate the effectiveness of non-temporal multiple silhouettes in HMM for recognizing posture.