Extensions of the CBMeMBer filter for joint detection,tracking, and classification of multiple maneuvering targets

This paper addresses the problem of joint detection, tracking and classification (JDTC) of multiple maneuvering targets in clutter. The multiple model cardinality balanced multi-target multi-Bernoulli (MM-CBMeMBer) filter is a promising algorithm for tracking an unknown and time-varying number of multiple maneuvering targets by utilizing a fixed set of models to match the possible motions of targets, while it exploits only the kinematic information. In this paper, the MM-CBMeMBer filter is extended to incorporate the class information and the class-dependent kinematic model sets. By following the rules of Bayesian theory and Random Finite Set (RFS), the extended multi-Bernoulli distribution is propagated recursively through prediction and update. The Sequential Monte Carlo (SMC) method is adopted to implement the proposed filter. At last, the performance of the proposed filter is examined via simulations.     

A Bayesian approach to joint tracking and identification of geometric shapes in video sequences

A Bayesian approach is proposed for joint tracking and identification. These two problems are often addressed independently in the literature, leading to suboptimal performance. In a Bayesian approach, a prior distribution is set on both the hypothesis space and the associated parameter space. Although this is straightforward from a conceptual viewpoint, it is typically impossible to perform inference in closed-form. We discuss an advanced particle filtering approach to solve this computational problem and apply this algorithm to joint tracking and identification of geometric forms in video sequences.