Robust Scheme for Detection of an Expanding Moving Object Using a Facet-Based Model in Infrared Imaging

Abstract

In detection and tracking of a small or large moving object in infrared (IR) imaging systems, it is necessary to perform analysis of the object in real time. The authors proposed the facet-based detection scheme for a small moving object with zero-mean Gaussian noise in previous research. However, it is difficult to detect larger moving objects using the facet-based model because the kernel size in the facet-based model is 5 x 5 pixels. In this article, the authors propose a robust detection scheme using the facet-based model in IR for larger moving objects. A new condition for the object is proposed for the robust facet-based detection of a larger object with zero-mean Gaussian noise. In the proposed algorithm, first, we extract a mean of image intensity from the center of the facet in the region of interest (ROI) of the first frame. Second, we apply the facet-based model to the same positioned pixel in a subsequent frame. The pixels are detected from the maximum extreme condition. The pixels are detected from the maximum extreme condition. The experimental results show that the proposed algorithm is efficient and robust. (C) 2010 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2010.54.2.020506]