Detecting small, moving objects in image sequences using sequentialhypothesis testing
Blostein, S.D.; Huang, T.S.
Signal Processing, IEEE Transactions on
Volume 39, Issue 7, Jul 1991 Page(s):1611 - 1629
Digital Object Identifier 10.1109/78.134399
Summary:An algorithm is proposed for the solution of the class of
multidimensional detection problems concerning the detection of small,
barely discernible, moving objects of unknown position and velocity in a
sequence of digital images. A large number of candidate trajectories,
organized into a tree structure, are hypothesized at each pixel in the
sequence and tested sequentially for a shift in mean intensity. The
practicality of the algorithm is facilitated by the use of multistage
hypothesis testing (MHT) for simultaneous inference, as well as the
existence of exact, closed-form expressions for MHT test performance in
Gaussian white noise (GWN). These expressions predict the algorithm's
computation and memory requirements, where it is shown theoretically
that several orders of magnitude of processing are saved over a
brute-force approach based on fixed sample-size tests. The algorithm is
applied to real data by using a robust preprocessing procedure to
eliminate background structure and transform the image sequence into a
residual representation, modeled as GWN. Results are verified
experimentally on a variety of video image sequences
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