Optimal multiframe detection and tracking in digital image sequences

Authors:
M. G. S. Bruno;J. M. F. Moura
Affiliations:
Electr. Eng. Dept., Sao Paulo Univ., Brazil;-
Venue:
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Year:
2000

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Sequential Monte Carlo methods for joint detection and tracking of multiaspect targets in infrared radar images

EURASIP Journal on Advances in Signal Processing

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Abstract

We present a Bayesian algorithm for optimal multiframe detection and tracking of small extended targets in two-dimensional (2D) finite resolution images. The algorithm integrates detection and tracking into a single framework using as data a sequence of cluttered sensor snapshots. Performance studies using Monte Carlo simulations show substantial improvements when the proposed Bayes tracker is compared to the association of a correlation filter and a linearized Kalman-Bucy filter. Likewise, there are significant detection performance gains of up to 6 dB in peak signal-to-noise ratio (PSNR) when the multiframe Bayes detector is compared to a single frame likelihood ratio test (LRT) detector.