Target Tracking And Recognition Based On View And Identity Manifolds

Automated target tracking and recognition (ATR) is an important capability in many military and civilian applications. A major challenge in vision-based ATR is how to cope with the variations of target appearances due to different viewpoints and underlying3D structures. Both factors, identity in particular, are usually represented by discrete variables in practical existing ATR algorithms. On the other hand, some ATR approaches depend on the use of multi-view exemplar templates to train a classifier. Such methods normally require a dense set of training views for successful ATR tasks and they are often limited in dealing with unknown targets.In this paper we will account for both factors in a continuous manner by using view and identity manifolds. Coupling the two manifolds for target representation facilitates the ATR process by allowing us to meaningfully synthesize new target appearances to deal with previously unknown targets as well as both known and unknown targets under previously unseen viewpoints.In this work, we mainly focus on tracking and recognition techniques for visible imagery, which is a preferred imaging modality for most civilian applications. Common target representations are non-parametric in nature. The shape variability due to different structures and poses is characterized explicitly using a deformable and parametric model that must be optimized for localization and recognition. This method requires high-resolution images where salient edges of a target can be detected, and may not be appropriate for ATR in practical imagery. In this work, we propose a new couplet of identity and view manifolds for multi-view shape modeling. The1D identity manifold captures both inter-class and intra-class shape variability. The2D hemispherical view manifold is used deal with view variations for ground vehicles.We use a nonlinear tensor decomposition technique to integrate these two manifolds into a compact generative model. Because the two variables, view and identity, are continuous in nature and defined along their respective manifolds, the ATR inference can be efficiently implemented by means of a particle filter where tracking and recognition can be accomplished jointly in a seamless fashion. We evaluate this new target model against the MATLAB database that contains a rich set of3D imagery depicting various military and civilian vehicles. To examine the efficacy of the proposed target model, we develop four ATR algorithms based on different ways of handling the view and identity factors. The experimental results demonstrate the advantages of coupling the view and identity manifolds for shape interpolation, both qualitatively and quantitatively. The tracking and recognition rate reach to85%.