An Efficient Matching Kernel Human Detection Method Based on Fast and Robust Features
A human body detection and robust technology, applied in the field of image processing, can solve problems such as interference of detection results, achieve good detection results, avoid local matching errors, and reduce calculation time and data calculation.
Active Publication Date: 2017-02-08
XIDIAN UNIV
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This method extracts the HOG feature of the gradient histogram and combines the feature template at the same time, which increases the function of head feature discrimination, improves the detection rate compared with the traditional method, and has a good feature recognition effect especially for the image space with little background change. , however, this method still has the disadvantage that the detection results will be disturbed in the case of mixed background or uneven illumination
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[0046] The present invention will be further described below in conjunction with the accompanying drawings.
[0047] Refer to attached figure 1 , the concrete steps of the present invention are as follows:
[0048] Step 1. Select training sample set images.
[0049]Using a bootstrap operation, enough negative images are obtained from the non-human natural images of the INRIA database.
[0050] The specific steps of the bootstrap operation are as follows:
[0051] In the first step, m positive sample images and n negative sample images are randomly selected from the INRIA database, among which 100≤m≤500, 100≤n≤800, and n≤m≤3n, using the gradient orientation histogram HOG feature extraction method, Feature extraction is performed on all the selected positive and negative sample images, and the SVM classifier is used to perform classification training on the extracted features to obtain the initial classifier.
[0052] The second step is to continuously randomly select non-hu...
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The invention provides an efficient matching kernel body detection method based on rapid robustness characteristics. The efficient matching kernel body detection method mainly solves the problem that a traditional method can not well solve the problem of image background mixing or uneven illumination. The efficient matching nuclear body detection method includes the first step of selecting training sample set images, the second step of extracting SURF characteristic points of the images, the third step of building an initial vector basis of each layer, the fourth step of obtaining the biggest kernel function characteristic of a sampling layer, the fifth step of obtaining image efficient matching kernel characteristics, the sixth step of carrying out classified training, the seventh step of imputing the images to scan, the eighth step of detecting a scanning window, and the ninth step of outputting detection results. Through layered extraction of local information of the images to carry out characteristic learning, the characteristics are mapped to a low-dimensional space and are gathered into a characteristic set, then a linear classifier is used for training the characteristics, and a body detection classifier is obtained. The efficient matching kernel body detection method can be used for accurately detecting body information in natural images in the field of image processing.
Description
technical field [0001] The invention belongs to the technical field of image processing, and further relates to a fast and robust feature-based Efficient Match Kernel (EMK) human detection method in the technical field of static human detection. The invention can be used to detect human body information from static images to achieve the purpose of identifying human body targets. Background technique [0002] Human body detection is the process of judging the location of human body information from natural images. In recent years, due to its application value in the fields of intelligent monitoring, driver assistance systems, human motion capture, and pornographic image filtering, it has become a computer vision field. key technology. However, human body detection has become a very difficult problem due to the diversity of human body poses, background clutter, clothing textures, lighting conditions, and self-occlusion. At present, the methods of human detection in static im...
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IPC IPC(8): G06K9/66
Inventor 韩红焦李成郭玉言马文萍马晶晶侯彪祝健飞
Owner XIDIAN UNIV