Abstract

In this paper, we introduce a novel surveillance system based on thermal catadioptric omnidirectional (TCO) vision. The conventional contour-based methods are difficult to be applied to the TCO sensor for detection or tracking purposes due to the distortion of TCO vision. To solve this problem, we propose a contour coding based rotating adaptive model (RAM) that can extract the contour feature from the TCO vision directly as it takes advantage of the relative angle based on the characteristics of TCO vision to change the sequence of sampling automatically. A series of experiments and quantitative analyses verify that the performance of the proposed RAM-based contour coding feature for human detection and tracking are satisfactory in TCO vision.

© 2012 Optical Society of America

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