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Proceedings Paper

Hybrid methodology for the detection, tracking, and classification of humans in difficult infrared video
Author(s): James R. Bonick
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Paper Abstract

The detection, tracking, and classification of humans in video imagery is of obvious military and civilian importance. The problem is difficult under the best of circumstances. In infrared (IR) imagery, or any grayscale imagery, the problem is compounded by the lack of color cues. Sometimes, human detection in IR imagery can take advantage of the thermal difference between humans and background-but this difference is not robust. Varying environmental conditions regularly degrade the thermal contrast between humans and background. In difficult data, humans can be effectively camouflaged by their environment and standard feature detectors are unreliable. The research described here uses a hybrid approach toward human detection, tracking, and classification. The first is a feature-based correlated body parts detector. The second is a pseudo-Hough transform applied to the edge images of the video sequence. The third relies on an optical flow-based vector field transformation of the video sequence. This vector field permits a multidimensional application of the feature detectors initiated in the previous two methods. Then a multi-dimensional oriented Haar transform is applied to the vector field to further characterize potential detections. This transform also shows potential for distinguishing human behavior.

Paper Details

Date Published: 3 May 2012
PDF: 7 pages
Proc. SPIE 8391, Automatic Target Recognition XXII, 839102 (3 May 2012); doi: 10.1117/12.915512
Show Author Affiliations
James R. Bonick, U.S. Army Night Vision and Electronic Sensors Directorate (United States)


Published in SPIE Proceedings Vol. 8391:
Automatic Target Recognition XXII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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