Self-adaptive cascade classifier training method based on online learning

Abstract

The invention discloses a self-adaptive cascade classifier training method based on online learning, which comprises the following steps: (1), preparing a training sample set with a small quantity of samples, and training an initial cascade classifier HC(x) in a cascade classifier algorithm; (2), using the HC(x) for traversal of image frames to be detected, extracting areas with sizes identical to the sizes of the training samples one by one, calculating a feature value set, classifying the areas with the initial cascade classifier, and judging whether the areas are target areas, thereby completing target detection; (3) tracking the detected targets in a particle filtering algorithm, verifying the target detection results through tracking, marking detection with errors as a negative sample for online learning, obtaining different attitudes of real targets through tracking and extracting a positive sample for online learning; and (4) carrying out online training and updating for the initial cascade classifier HC(x) in a self-adaptive cascade classifier algorithm when an online learning sample is obtained, thereby gradually improving the target detection accuracy of the classifier.

Description

technical field The invention belongs to the field of pattern recognition, and in particular relates to a method for training a classifier through an online learning algorithm to improve the performance of the classifier. Background technique Online learning belongs to the research category of incremental learning. In this type of method, the classifier only learns each sample once instead of repeated learning. In this way, a large amount of storage space is not required to store training samples during the operation of the online learning algorithm. Every time the classifier obtains a sample, it conducts online learning. Through online learning, the classifier can still update and improve itself according to the new sample during use, further improving the classification effect Early online learning algorithms include Winnow algorithm, unified linear prediction algorithm, etc. In 2001, scholar Oza combined these algorithms with boosting algorithm and proposed online boosti...

AI Technical Summary

Problems solved by technology

However, online Adaboost uses feature selection operators instead of general weak classifiers to synthesize strong classifiers. The number of feature selection operators and the number of weak classifiers corresponding to feature selection operators are fixed, and the corresponding online learning classifier structure is relatively rigid.

When it is found that its classification ability cannot meet the requirements of detection performance, even continuous online learning cannot improve the detection accuracy

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