Movement recognition exploiting multi-view information
Alexandros Iosifidis, Nikos Nikolaidis, Ioannis Pitas · 2010
In this paper a novel view-invariant movement recognition method is presented. A multi-camera setup is used to capture the movement from different observation angles. Identification of the position of each camera with respect to the subject's body is achieved by a procedure based on morphological operations and the proportions of the human body. Binary body masks from frames of all cameras, consistently arranged through the previous procedure, are concatenated to produce the so-called multi-view binary mask. These masks are rescaled and vectorized to create feature vectors in the input space. Fuzzy vector quantization is performed to associate input feature vectors with movement representations and linear discriminant analysis is used to map movements in a low dimensionality discriminant feature space. Experimental results show that the method can achieve very satisfactory recognition rates.