Human detection method based on non-negative matrix factorization

Abstract

The invention provides a human detection method based on non-negative matrix factorization, which mainly solves the problems of high feature dimension and high computational load of the existing method. The human detection method is implemented by the following processes of: extracting a scale and rotational invariance SIFT feature point of a training sample image in a CVC-02 database; executing non-negative matrix factorization on the scale and rotational invariance SIFT feature point to get one basis matrix and a coefficient matrix; orthogonalizing and transposing the basis matrix, then, multiplying the basis matrix with the scale and rotational invariance SIFT feature point of the training sample image to get the feature of the training sample image; executing classification training on the obtained feature by an SVM (Support Vector Machine) classifier to obtain a detection classifier; inputting the features of the to-be-detected image into the classifier, according to the classification result, combining all the scanning windows divided for the human to form the final human detection result. The human detection method has the advantages of low feature dimension and high detection accuracy, and can be used for classification and detection on human bodies and other targets in the image.

Description

technical field [0001] The invention belongs to the technical field of computer vision and pattern recognition, relates to a human body detection method, and can be used for detecting human bodies and other objects in images. Background technique [0002] Human detection has many important applications in computer vision, such as video surveillance, smart cars and smart transportation, robotics and advanced human-computer interaction, etc. However, the appearance of the human body varies greatly due to factors such as changes in the human body's own posture, diversity of clothing, and illumination, making human body detection a very difficult problem. [0003] At present, the methods of human body detection in images mainly include methods based on human body models, methods based on template matching and methods based on statistical classification. [0004] The method based on the human body model requires a clear human body model, and then performs human body recognition ...

AI Technical Summary

Problems solved by technology

The advantage of the method based on statistical classification is that the detection result is stable and the effect is better. The disadvantage is that the feature dimension usually extracted is large, the amount of calculation is large, and it is difficult to solve the problems of insufficient light and mixed background.

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