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Real-time human body detection method based on AdaBoost frame and colour of head

A technology of human body detection and head, which is applied in computer parts, character and pattern recognition, instruments, etc., can solve the problems of poor detection effect and fast detection speed, improve head feature discrimination, reduce false alarm rate, and improve The effect of detecting the effect

Inactive Publication Date: 2011-08-24
HARBIN ENG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

Due to the small number of features and the AdaBoost-Boosting classification method, the detection speed is fast, but the detection effect is slightly worse

Method used

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  • Real-time human body detection method based on AdaBoost frame and colour of head
  • Real-time human body detection method based on AdaBoost frame and colour of head
  • Real-time human body detection method based on AdaBoost frame and colour of head

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Embodiment Construction

[0030] The following examples describe the present invention in more detail:

[0031] 1 training ---- HOG feature extraction

[0032] 1.1 Feature extraction process

[0033] The feature extraction process is as follows.

[0034] (1) Make a feature template (see Section 1.3 for specific methods), and save it;

[0035] (2) Load the feature template, and generate a feature mapping table according to the feature template (see Section 1.3 for the specific method);

[0036] (3) feature extraction according to the feature mapping table;

[0037] For each training sample image, feature extraction is done in the following steps.

[0038] (1) Scale the image to a size of 64×128;

[0039] (2) Use the mask of [-1 0 1] to calculate its gradient magnitude image and orientation angle image;

[0040] (3) Divide the image into cells whose basic units are 2, 4 and 8; calculate multi-scale cell features;

[0041] (4) Form feature vectors (see Section 1.3).

[0042] 1.2 Calculation of mul...

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Abstract

The invention provides a real-time human body detection method based on an AdaBoost frame and colour of a head, comprising training and detecting steps: (1) extracting multi-scale HOG (histograms of oriented gradients) characteristics according to a template; (2) training a human body detection model by adopting an AdaBoost-Boosting method; (3) extracting histogram characteristics of the colour of the head; (4) training a head discrimination model by adopting an AdaBoost method; (5) detecting a human body based on a sliding window method; (6) for each detection window, firstly extracting the HOG characteristics, and judging whether the detected object is a human body or not according the human body detection model; and (7) for the window which is determined to be a human body, extracting the histogram characteristics of the head, judging whether the head is contained or not; and determining the window containing the head to be a window containing a human body, and drawing a rectangle at a corresponding position in an image. In the invention, the characteristic unit, namely a Block of the original HOG characteristics is adopted, the Block is of multi-scale type, and a characteristic template is combined, thus the detection effect is improved; and the head characteristic discrimination is added, thus the detection rate is improved.

Description

technical field [0001] The present invention relates to a human body detection method, in particular to a real-time human body detection method based on the AdaBoost framework and head color features. Background technique [0002] Human detection has a wide range of applications in surveillance systems, driver assistance systems, and disaster scene search and rescue. Human detection in cluttered backgrounds is an extremely challenging task. Many researchers have devoted themselves to human detection and achieved some results. The more successful detection method is the method of HOG (Histograms of Oriented Gradients, histogram of oriented gradients) combined with support vector machine (SVM), but the detection speed of this method is slow. [0003] Begard studied different types of AdaBoost algorithms for pedestrian detection (J.Begard, N.Allezard, P.Sayd.Real-time human detection in urban scenes: Local descriptors and classifiers selection with adaboost-like algorithms.In...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 李智慧邵春艳李香
Owner HARBIN ENG UNIV
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