Fully-autonomous on-line study method based on random fern classifier

Abstract

The invention discloses a fully-autonomous on-line study method based on a random fern classifier. With the method, on-line study targeted for a classifier of the target class can be carried out by selecting a target in a video frame for only once. The method comprises the following steps: carrying out affine transformation on the selected target to obtain an initial positive sample set, and extracting a small number of negative sample sets in a non- target area of the video to train an initial random fern classifier; then, carrying out target detection in the video frame by using the classifier; in the detection process, using a nearest neighbor classifier to collect on-line study new samples and automatically judging the class of the samples; and finally, applying the new samples to on-line training of the random fern classifier, updating random fern posterior probability, improving the precision of target detection of the classifier gradually, and realizing fully-autonomous on-line study of a target detection system.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to a fully autonomous online learning method based on a random fern classifier. Background technique [0002] 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, and further improve the classification effect. [0003] 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 boosting algorithm (...

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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