AdaBoost cascade classifier rapid detection method

Abstract

The invention discloses an AdaBoost cascade classifier rapid detection method. The method comprises the following steps of: S1, training an AdaBoost-based primary cascade classifier; S2, testing detection performance of the primary cascade classifier after each stage of the one-stage cascade classifier is combined; S3, determining the number X of starting layers using a zoom window according to the detection performance of the primary cascade classifier after the combination; S4, detecting a sample library by using X layers of primary cascade classifiers and the combination operation, so as to determine detected positive samples and falsely detected negative samples; S5, retraining an AdaBoost secondary cascade classifier by using the detected positive samples and falsely detected negative samples; and S6, carrying out detection by adoption of a manner of combining a primary cascade classifier zoom classifier and a secondary cascade classifier zoom detection window. According to the method disclosed by the invention, a manner of combining the primary cascade classifier and the secondary cascade classifier is adopted, so that the detection performance of systems is further improved while the detection speed is ensured.

Description

technical field [0001] The invention belongs to the technical field of target detection and recognition, and relates to a fast detection method of an AdaBoost cascade classifier. Background technique [0002] In the target detection method, a class of methods commonly used now is the classifier design algorithm based on statistical learning. In machine learning methods, AdaBoost can combine weak classifiers to form a strong classifier, and has also been successfully applied in face detection and other aspects. In OpenCV (open source computer vision library), this algorithm has been successfully implemented and applied in combination with Haar features, HOG and other features, and is used by a large number of computer vision researchers, making it the first choice for target detection applications in various occasions. With the help of OpenCV, we can quickly apply the AdaBoost algorithm combined with various features to test the performance of the target detection applicatio...

AI Technical Summary

Problems solved by technology

When calculating the Haar feature, you can also use the integral image to quickly obtain the feature, and the calculation speed has a certain advantage. However, in terms of detection performance, due to the scaling of the classifier will bring a certain error, the detection performance is reduced compared to the second method.

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