Multi-object tracking via a recursive generalized likelihood approach
Dalton Porter, Thomas S. Englar · 1979
This paper deals with the problem of tracking multiple objects with multiple sensors where the association of measurements with objects is ambiguouus. Object motion is modeled as a random process moving locally about a mean path where the random process model can be one of a discrete set of possibilities. In the above setting, the tracking problem amounts to associating data with objects, selecting motion models for the objects and estimating the object state. The multi-object tracking problem is solved using the generalized likelihood approach. No a priori statistical information is used concerning the correctness of a data association hypothesis. A practical recursive algorithm is described that has been successfully applied to large scale surveillance problems.