We propose an algorithm to detect human intruders by means of Leaky Coaxial cables (LCX) for wide area surveillance systems. When a human intrudes into an area between two LCX, the radiated waves will suffer considerable changes with specific fluctuations. The former version of our detection algorithm had many false detections because the algorithm used only amplitudes of in-phase/quadrature-phase (I/Q) detector signals. To develop a sophisticated detection algorithm, we analyzes I/Q detector signals in detail and introduce some critical signal features appeared in I/Q detector signals to discriminate human intruders from background noises and other irrelevant objects. We successfully reduced many of the false detections compared to the performance of the former algorithm. In this paper we introduce our LCX-based surveillance system, and describe a human intruder detection algorithm based on I/Q detector signals with typical simulation results. Current problems and remaining tasks are also discussed.

This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the ranges by analyzing input images, the detection sensitivity can be improved. For this narrowing, we employ the property that image variations at neighboring image blocks have strong correlation, also known as "cooccurrence". This approach is essentially different from chronological background image updating or morphological postprocessing. Experimental results for real images demonstrate the effectiveness of our method.

Background subtraction is a useful and effective method for detecting moving objects in video images. Since this method assumes that image variations are caused only by moving objects (i.e., the background scene is assumed to be stationary), however, its applicability is limited. In this paper, we propose a background subtraction method that robustly handles various changes in the background. The method learns the chronological changes in the observed scene's background in terms of distributions of image vectors. The method operates the subtraction by evaluating the Mahalanobis distances between the averages of such image vectors and newly observed image vectors. The method we propose herein expresses actual changes in the background using a multi-dimensional image vector space. This enables the method to detect objects with the correct sensitivity. We also introduce an eigenspace to reduce the computational cost. We describe herein how approximate Mahalanobis distances are obtained in this eigenspace. In our experiments, we confirmed the proposed method's effectiveness for real world scenes.