Infrared target tracking, recognition and segmentation using shape-aware level set
Jiulu Gong, Guoliang Fan, Joseph Havlicek, Ningjun Fan, Derong Chen · 2013
A new probabilistic model called ATR-Seg for automated target tracking, recognition and segmentation is proposed that incorporates a shape constrained level set with a shape generative model along with motion model. The shape model involves a view-independent identity manifold and infinite identity-dependent view manifolds for multi-view and multi-target shape modeling. ATR-Seg applies the motion model to predict the state of the target (i.e., 3D position, pose and identity), and then uses a shape-aware level set energy functional to evaluate the tracking and segmentation results. A particle filtering-based method is used for sequential inference, where the level set energy functional is treated as the likelihood function. Experimental results obtained against the SENSIAC ATR database demonstrate the advantages of the proposed method compared with the two recent techniques that require target pre-segmentation via background subtraction.