Study on the cascade classifier in target detection under complex background

Abstract

Method of target detection and tracking is one of the hot topics in image processing and computer vision field, which is significant not only in military such as imaging guidance and military target tracking, but also for civil use such as security and monitoring and the intelligent man-machine interaction. Treating the feature matching problem as a more general equinoctial classification question, can turn the intractable high-dimensional problem to a classification problem and deplete computer complexity. This method is based on the law of large numbers and Bayes rule. In this paper we propose a non-hierarchy structure classifier, for which the equation for calculation is theoretically derived, and apply 1bitBP feature to the classifier; and for further reducing the amount of calculation, we use integral image and square integral image to variance classifier as preprocessor, and then use non-hierarchy classifier to handle the patches which meet the variance demand and use the nearest neighbor to further improve the accuracy, and finally realize target detection and tracking based on cascade classifier. Our experimental results show that the method proposed is far superior in calculation amount and processing precision, and is robust to scale changing and rotation, so the method proposed in this paper is of high practical value.