<title>Robust object detection under various environmental conditions</title>

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We present a method which is robust to environmental conditions, that detects the existence of space filling objects such as cars, people, etc. This method is unaffected by uniform or local variations in image brightness induced by lighting conditions, weather, shadow of other objects such as buildings, trees, clouds, etc. Moreover, it does not depend on the position, size, or background pattern of the regions, or the shapes and color of the objects to be detected. The method consists of the following processes. Normalize brightness of the target and the reference image using mean and variance of the brightness in respective regions. The reference image is the image which represents the background scene without objects and is taken from the same camera position as the target images. Calculate normalized principal component features from both sets of normalized image brightness. Use the features to construct a classifier by statistical learning. The proposed features are determined by the variance and covariance of brightness of both images. They are a better measure of the correspondence of two images than conventional features such as the correlation coefficient of image brightness or statistics calculated from a difference image. The method is applied to car detection in a parking lot. The experimental images were collected over a one year period under various conditions. At least 98% of the cars were always correctly detected. Application to moving-car detection and person detection in a hall are presented. Since the proposed algorithm for object detection is robust under various environmental conditions and is object independent, it is well suited to a wide range of facilities offering automatic surveillance, automatic counting, automatic recognition of scene situation, etc.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.