Joint view-identity manifold for infrared target tracking and recognition

We propose a new joint view-identity manifold (JVIM) for multi-view and multi-target shape modeling that is well-suited for automated target tracking and recognition (ATR) in infrared imagery. As a shape generative model, JVIM features a novel manifold structure that imposes a conditional dependency between the two shape-related factors, view and identity, in a unified latent space, which is embedded with one view-independent identity manifold and infinite identity-dependent view manifolds. A modified local linear Gaussian process latent variable model (LL-GPLVM) is proposed for JVIM learning where a stochastic gradient descent method is used to improve the learning efficiency. We also develop a local inference technique to speed up JVIM-based shape interpolation. Due to its probabilistic and continuous nature, JVIM provides effective shape synthesis and supports robust ATR inference for both known and unknown target types under arbitrary views. Experiments on both synthetic data and the SENSIAC infrared ATR database demonstrate the advantages of the proposed method over several existing techniques both qualitatively and quantitatively.