A Tree Search Algorithm for Target Detection in Image Sequences
Abstract
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 position 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. Second, a large, dense set of pixel-sized space-time trajectories are hypothesized and tested in the innovations sequence. The search space 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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Blostein and Huang. "A Tree Search Algorithm for Target Detection in Image Sequences." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1988. doi:10.1109/CVPR.1988.196309Markdown
[Blostein and Huang. "A Tree Search Algorithm for Target Detection in Image Sequences." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1988.](https://mlanthology.org/cvpr/1988/blostein1988cvpr-tree/) doi:10.1109/CVPR.1988.196309BibTeX
@inproceedings{blostein1988cvpr-tree,
title = {{A Tree Search Algorithm for Target Detection in Image Sequences}},
author = {Blostein, Steven D. and Huang, Thomas S.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1988},
pages = {690-695},
doi = {10.1109/CVPR.1988.196309},
url = {https://mlanthology.org/cvpr/1988/blostein1988cvpr-tree/}
}