Human motion identification method based on second generation Bandelet statistical characteristics

Abstract

The invention discloses a human motion identification method based on second generation Bandelet statistical characteristics, which mainly solves the problems of complicated characteristic extraction and poor representational capacity in the prior art. The human motion identification method comprises the steps of: 1, converting video in a Weizmann database into a sequence image, constructing a training sample set X and a test sample set T according to a proportion of 8:1; 2, carrying out second generation Bandelet transformation on a single sequence image in the sample sets, sequentially extracting an energy characteristic Ve, an entropy characteristic Vs, a maximum value characteristic Vmax, a minimum value characteristic Vmin, a contrast ratio characteristic Vc, a mean value characteristic Vmu and a variance characteristic Vv of the image, and cascading all the characteristics to be used as final characteristics of the single image; and 3, repeating the step 2 for respectively extracting characteristics of all sequence images in the training sample set X and the test sample set T to obtain a training sample characteristic set X* and a test sample characteristic set T*, and carrying out learning training on the training sample characteristic set X* and the test sample characteristic set T* by using an Adaboost algorithm to obtain a classifying result. The human motion identification method can be used for accurately identifying a human motion, and can be used in video monitoring and video processing of target identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a human motion recognition method, and can be used for virtual video, intelligent monitoring and attitude estimation. Background technique [0002] Human motion recognition is one of the major hotspots in the field of computer vision in recent years. Human motion recognition has been initially applied in many fields such as motion capture, human-computer interaction, and video surveillance, and has great application prospects. Due to the variability and diversity of human motion, noisy background, lighting conditions, clothing texture and self-occlusion and other factors seriously affect the recognition effect of human motion, it is important to accurately estimate human body posture from video images and realize human motion recognition. A long-standing problem in computer vision. [0003] At present, the methods of human motion recognition are mainly divided into three cate...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose a human motion recognition method based on the second-generation Bandelet statistical features, to reduce the complexity of image feature extraction, and improve the representation ability of features at the same time, without the need for a large amount of training data effectively improve the accuracy of human motion recognition

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