A comparison of detection performance for several track-before-detect algorithms
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing
Multichannel dual domain infrared target tracking for highly evolutionary target signatures
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Relative entropy rate based multiple hidden Markov model approximation
IEEE Transactions on Signal Processing
Multi-aspect target tracking in image sequences using particle filters
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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In this paper, we introduce new algorithms for automatic tracking of multiaspect targets in cluttered image sequences. We depart from the conventional correlation filter/Kalman filter association approach to target tracking and propose instead a nonlinear Bayesian methodology that enables direct tracking from the image sequence incorporating the statistical models for the background clutter, target motion, and target aspect change. Proposed algorithms include 1) a batch hidden Markov model (HMM) smoother and a sequential HMM filter for joint multiframe target detection and tracking and 2) two mixed-state sequential importance sampling trackers based on the sampling/importance resampling (SIR) and the auxiliary particle filtering (APF) techniques. Performance studies show that the proposed algorithms outperform the association of a bank of template correlators and a Kalman filter in adverse scenarios of low target-to-clutter ratio and uncertainty in the true target aspect.