Vehicle classifier training method

Abstract

The invention provides a vehicle classifier training method, which comprises the following steps: utilizing a few samples to train an initial classifier; converting videos collected by a camera sensor into pictures; carrying out detection on the pictures through the initial classifier; then, keeping the pictures passing detection of the initial classifier and pictures comprising a vehicle target but failing the detection of the classifier, wherein the pictures passing detection of the initial classifier may comprise correct detected vehicle pictures and wrong detected vehicle pictures; defining the wrong detected vehicle pictures as a negative sample hard example, and defining the pictures comprising the vehicle target but failing the detection of the classifier as a positive sample hard example; and adding the positive sample hard example and the negative sample hard example to a positive and negative sample library and continuing training a vehicle classifier until detection rate and false detection rate of the trained classifier reach requirements of clients. The method can increase diversity of classifier training samples, improve the detection rate of vehicle detection and reduce the false detection rate, and finally, optimizes the classifier training flow.

Description

technical field [0001] The invention relates to the technical field of vehicle image recognition and detection, in particular to a training method for a vehicle classifier. Background technique [0002] At this stage, vehicle image recognition and detection technology has been applied and paid attention to in the field of automobile assisted safe driving, and improving the detection rate and reducing the false detection rate are key indicators in the application of vehicle image recognition and detection technology. [0003] In the prior art, the vehicle image recognition and detection technology uses classifiers to learn to detect the representation of the target, and most of the classifiers are trained based on HaaR, HOG, and LBP features, or use Adaboost learning algorithm, or SVM learning algorithm trained. The classifiers in the prior art use the above technical solutions for training and learning, which may easily lead to insufficient memory, long training time, and e...

AI Technical Summary

Problems solved by technology

The classifiers in the prior art use the above technical solutions for training and learning, which may cause insufficient memory and long training time as the number of detection samples increases, and even cause the training process of the classifier to be paralyzed.

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