Integrating shape and dynamic probabilistic models for data association and tracking

Tracking and data association procedures like the joint probabilistic data association filter (JPDAF) are not prone to the integration of additional information, such as shape constraints. A standard probabilistic framework is not suited to merging partially incoherent information sources. The theory of evidence, introduced by Shafer, describes a way to combine distinct "bodies of evidence" about the same phenomena. Under this framework we provide a rigorous derivation of the JPDAF as well as a procedure to integrate additional shape knowledge.