Performance analysis on an efficient human motion database with various motion representations

In this paper, our proposed structured human motion database is adopted for different motion representations. The motions are first represented as a sequence of frames of 2D images, which were compressed using three recognized motion representation techniques: exclusive-OR, MEI (motion energy image), and MHI (motion history images). The representation is a 2D feature image. The feature image is compressed by characterizing the eigenvectors. A complete vector space called an eigenspace is constructed that represents the image feature vectors for the feature image. The motions are indexed using the projections onto the eigenspace. For the purpose of efficient searching within the database, our proposed B-tree motion database is created and maintained. The comparative performance evaluations for the aforesaid representations were investigated and satisfactory performances (about 90% recognition rate and smaller searching time) were realized for all of the cases using our proposed motion database structure.