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|Title:||Interacting multiple-model-based tracking of multiple point targets using an expectation maximization algorithm in an infrared image sequence|
|Publisher:||SPIE-INT SOCIETY OPTICAL ENGINEERING|
|Citation:||OPTICAL ENGINEERING, 44(1), -|
|Abstract:||Data association and model selection are important factors for tracking multiple targets in a dense clutter environment without using a priori information about the target dynamic. We propose an interacting multiple model-expectation maximization (IMM-EM) algorithm by incorporating different dynamic models for the target and using Markov random field (MRF) for data association. In this way we are able to track maneuvering and nonmaneuvering targets simultaneously in a single batch mode (sequential). Moreover, it can be used for real-time application. The proposed method overcomes the problem of data association by incorporating all validated measurements together using an EM algorithm and exploiting MRF It treats the data association problem as an incomplete data problem and measurement association as missing data. In the proposed method, all validated measurements are used to update the target state, and the probability density function (pdf) of observed data, given a target state and measurement association, is treated as a mixture pdf. This allows us to combine the likelihood of a measurement due to each model. The association process now incorporates IMM, and consequently, it is possible to track any arbitrary trajectory. We also consider two different cases for association of measurement to target: association of each measurement to the target is independent of each other; and association of a measurement influences an association of its neighbor measurement. (C) 2005 Society of Photo-Optical Instrumentation Engineers.|
|Appears in Collections:||Article|
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