Joint view-identity manifold for target tracking and recognition

A new joint view-identity manifold (JVIM) is proposed for multiview shape modeling that is applied to automated target tracking and recognition (ATR). This work improves our recent work where the view and identity manifolds are assumed to be independent for multi-view multi-target modeling. A local linear Gaussian process latent variable model (LL-GPLVM) is used to learn a probabilistic JVIM which can capture both inter-class and intra-class variability of 2D target shapes under arbitrary view point jointly in one coexisted latent space. A particle filter-based ATR algorithm is developed to simultaneously infer the view and identity parameters along JVIM so that target tracking and recognition can be achieved jointly in a seamlessly fashion. The experimental results using SENSIAC ATR database demonstrate the advantages of our method both qualitatively and quantitatively compared with existing methods using template matching or separate view and identity manifolds.