View indepedent human movement recognition from multi-view video exploiting a circular invariant posture representation
In this paper a novel method for view independent human movement representation and recognition, exploiting the rich information contained in multi-view videos, is proposed. The binary masks of a multi-view posture image are first vectorized, concatenated and the view correspondence problem between train and test samples is solved using the circular shift invariance property of the discrete Fourier transform (DFT) magnitudes. Then, using fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA), different movements are represented and classified. This method allows view independent movement recognition, without the use of calibrated cameras, a-priori view correspondence information or 3D model reconstruction. A multiview video database has been constructed for the assessment of the proposed algorithm. Evaluation of this algorithm on the new database, shows that it is particularly efficient and robust, and can achieve good recognition performance.