Tree search algorithm for target detection in image sequences.

Steven D. Blostein, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Given a time sequence of digital images of a high-noise environment, the authors address the problem of detecting pixel-sized, barely discernible moving objects whose positions and trajectories are unknown. The sequences may be temporally sparse and contain significant frame-to-frame drifting background clutter, as caused by relative motion between the sensor array and natural terrain, ocean, or clouds. A general, two-step approach is presented. First, time correlation and space-varying background structure are removed. The resulting innovations sequence is modeled by an independent and identically distributed (i.i.d.) Gaussian random field. Second, a large, dense set of pixel-sized space-time trajectories are hypothesized and tested in the innovations sequence. The search space, typically containing thousands of trajectories per pixel per image, is organized into a tree structure. A sequential statistical technique, multistage hypothesis testing, optimized for the innovations model, is used to test the multiple hypotheses and prune the tree-structured list of candidate trajectories. This results in an efficient algorithm with analyzable performance and processing requirements.

Original languageEnglish (US)
Title of host publicationProc CVPR 88 Comput Soc Conf on Comput Vision and Pattern Recognit
PublisherPubl by IEEE
Number of pages6
ISBN (Print)0818608625
StatePublished - 1988

Publication series

NameProc CVPR 88 Comput Soc Conf on Comput Vision and Pattern Recognit

ASJC Scopus subject areas

  • Engineering(all)


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