Similarity-weight-semi-supervised-dictionary-learning-based human behavior identification method

Abstract

The invention discloses a similarity-weight-semi-supervised-dictionary-learning-based human behavior identification method. With the method, a problem of low human behavior identification rate of the existing supervision method in the prior art can be solved. The identification method comprises: (1), an inputted data set is divided into test samples and training samples; (2), local feature detection is carried out on all samples and local features with the labeled samples are selected randomly to obtain an initialized dictionary; (3), according to the initialized dictionary, dictionary learning is carried out by using a semi-supervised method; (4), group sparse coding is carried out on all samples by using the learned dictionary to obtain a coding matrix of each sample; (5), vectorization is carried out on the coding matrix of each sample to obtain a final expression; and (6), testing sample classification is carried out by using the final expression of each sample and a sparse representation classification method to complete human behavior identification in the testing samples. Therefore, discrimination of dictionary learning is enhanced; the human behavior identification rate is improved; and the method can be used for target detection in a video.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, in particular to a method for recognizing the behavior of a target person in a video, which can be used for target detection in the video. Background technique [0002] Human behavior recognition refers to identifying the behavior information of the target in the video sequence and preparing for the subsequent processing work, which includes detecting relevant target visual information from the video sequence, expressing it in an appropriate way, and finally interpreting the information To achieve learning and recognition of human behavior. [0003] In recent years, unsupervised and supervised dictionary learning have been successfully applied in the fields of image classification and action recognition. In the field of human action recognition, whether they are distinguished using labeled video sequences, unsupervised dictionary learning does not use video label information, and sup...

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Problems solved by technology

[0009] Although the above method can obtain a more discriminative dictionary and improve the recognition accuracy, the shortcomings of this method are also obvious: it only considers the marked samples, does not consider the information of unmarked samples, and does not make full use of the sample information; and the actual On the Internet, it is often very difficult to obtain labeled samples, but unlabeled samples can be easily obtained and exist in large numbers. How to fully extract and utilize the information of a large number of unlabeled samples has become the key to this field.

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