Pedestrian detection method based on incremental learning

Abstract

The invention discloses a pedestrian detection method based on incremental learning. The pedestrian detection method based on the incremental learning can be used in the field of pedestrian detection in the background of big data. The pedestrian detection method based on the incremental learning comprises the following steps: firstly, an initial classifier is obtained by utilizing an image sample to intensively train a classifier; secondly, a novel training set is utilized to carry out incremental learning of the classifier, and a renewed classifier is obtained; thirdly, an image to be detected is detected by utilizing the renewed classifier to obtain a pedestrian detection result. The pedestrian detection method based on the incremental learning can occupy little internal storage in short time, and utilizes a plurality of continuously collected training sets in the background of big data to effectively carry out study and renew the classifier. Compared with a common method, the pedestrian detection method based on the incremental learning is high in speed, small in occupied internal storage, and convenient to use, and a classifying effect quite similar to the common method can be learnt.

Description

technical field [0001] The invention relates to pattern recognition and machine learning, in particular to a pedestrian detection method of incremental learning. Background technique [0002] In the traditional pedestrian detection method, the features extracted from the image are usually trained with a support vector machine to obtain a classifier. This method is feasible when there are not many training samples. In the context of big data, the training data is often very large and is collected successively. Every time a new training data set is collected, the data in the previous training set and the data in the new training set need to be combined for training. This operation is complicated, and the support vector machine needs to be retrained each time, which also consumes a lot of time and memory capacity. In particular, if the training data is extremely large, the memory capacity may not be sufficient for training the SVM. [0003] In addition, conventional increme...

AI Technical Summary

Problems solved by technology

This operation is complicated, and the support vector machine needs to be retrained every time, which also consumes a lot of time and memory capacity.

In particular, if the training data is particularly large, the memory capacity may not be sufficient for training the SVM

[0003] In addition, conventional incremental learning algorithms (such as stochastic gradient descent) have limited learning ability, and there is a big gap with the classifier learned by using support vector machine

Images