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Robust Small-Object Detection for Outdoor Wide-Area Surveillance

Daisuke ABE, Eigo SEGAWA, Osafumi NAKAYAMA, Morito SHIOHARA, Shigeru SASAKI, Nobuyuki SUGANO, Hajime KANNO

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Summary :

In this paper, we present a robust small-object detection method, which we call "Frequency Pattern Emphasis Subtraction (FPES)", for wide-area surveillance such as that of harbors, rivers, and plant premises. For achieving robust detection under changes in environmental conditions, such as illuminance level, weather, and camera vibration, our method distinguishes target objects from background and noise based on the differences in frequency components between them. The evaluation results demonstrate that our method detected more than 95% of target objects in the images of large surveillance areas ranging from 30-75 meters at their center.

Publication
IEICE TRANSACTIONS on Information Vol.E91-D No.7 pp.1922-1928
Publication Date
2008/07/01
Publicized
Online ISSN
1745-1361
DOI
10.1093/ietisy/e91-d.7.1922
Type of Manuscript
Special Section PAPER (Special Section on Machine Vision and its Applications)
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