Paper
1 August 1991 Multitarget adaptive gate tracker with linear prediction
Zhili Liu
Author Affiliations +
Abstract
After the stage of pointlike target tracking, the tracker comes to the stage of area target tracking. This paper considers the situation in which the FLIR image contains a number of targets. The targets are compact. The image segmentation is achieved by considering not only the global gray-level distribution but also the spatial distribution of target pixels. After the global gray-level statistics in the whole image, a recursive merging algorithm, a hierarchical clustering on the gray level and spatial position, is applied to segment the image into a multiple gray-level image. To make full use of the whole scene scan, a four direction code method is used to depict the edges of target regions. And to save more execution time, the useful features, including structure feature and geometry feature, are calculated by the four direction code expression at the same time. Thus the target edge description and the useful features calculation are finished in the same whole scene scan of the segmented image. A changing index table which describes targets and their features within the segmented image is set up. The index table is updated at each scene to depict the changing content of the image. During the time in which the image gets into a comparable stable state, a target recognition procedure may be applied to find the interested target, and the threatening value of each target is considered. An adaptive gate is assigned to each interested target.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhili Liu "Multitarget adaptive gate tracker with linear prediction", Proc. SPIE 1482, Acquisition, Tracking, and Pointing V, (1 August 1991); https://doi.org/10.1117/12.29248
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KEYWORDS
Image segmentation

Target recognition

Information technology

Target detection

Target acquisition

Detection and tracking algorithms

Edge detection

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