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<title>Object Detection Using Haar-like Features with Cascade of Boosted
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Classifiers</title>
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<div class=Section1>
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<h1><span lang=EN-US>Rapid Object Detection With A Cascade of Boosted
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Classifiers Based on Haar-like Features</span></h1>
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<h2><span lang=EN-US>Introduction</span></h2>
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<p class=MsoNormal><span lang=EN-US>This document describes how to train and
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use a cascade of boosted classifiers for rapid object detection. A large set of
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over-complete haar-like features provide the basis for the simple individual
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classifiers. Examples of object detection tasks are face, eye and nose
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detection, as well as logo detection. </span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>The sample detection task in this document
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is logo detection, since logo detection does not require the collection of
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large set of registered and carefully marked object samples. Instead we assume
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that from one prototype image, a very large set of derived object examples can
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be derived (</span><span class=Typewch><span lang=EN-US>createsamples</span></span><span
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lang=EN-US> utility, see below).</span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>A detailed description of the training/evaluation
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algorithm can be found in [1] and [2].</span></p>
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<h2><span lang=EN-US>Samples Creation</span></h2>
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<p class=MsoNormal><span lang=EN-US>For training a training samples must be
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collected. There are two sample types: negative samples and positive samples.
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Negative samples correspond to non-object images. Positive samples correspond
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to object images.</span></p>
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<h3><span lang=EN-US>Negative Samples</span></h3>
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<p class=MsoNormal><span lang=EN-US>Negative samples are taken from arbitrary
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images. These images must not contain object representations. Negative samples
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are passed through background description file. It is a text file in which each
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text line contains the filename (relative to the directory of the description
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file) of negative sample image. This file must be created manually. Note that
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the negative samples and sample images are also called background samples or
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background samples images, and are used interchangeably in this document</span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>Example of negative description file:</span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>Directory structure:</span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>/img</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US><EFBFBD> img1.jpg</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US><EFBFBD> img2.jpg</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>bg.txt</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US> </span></span></p>
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<p class=MsoNormal><span class=Typewch><span style='font-family:"Times New Roman";
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font-weight:normal'>File </span></span><span class=Typewch><span lang=EN-US>bg.txt:</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>img/img1.jpg</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>img/img2.jpg</span></span></p>
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<h3><span lang=EN-US>Positive Samples</span></h3>
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<p class=MsoNormal><span lang=EN-US>Positive samples are created by </span><span
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class=Typewch><span lang=EN-US>createsamples</span></span><span lang=EN-US>
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utility. They may be created from single object image or from collection of
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previously marked up images.<br>
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<br>
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</span></p>
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<p class=MsoNormal><span lang=EN-US>The single object image may for instance
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contain a company logo. Then are large set of positive samples are created from
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the given object image by randomly rotating, changing the logo color as well as
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placing the logo on arbitrary background.</span></p>
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<p class=MsoNormal><span lang=EN-US>The amount and range of randomness can be
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controlled by command line arguments. </span></p>
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<p class=MsoNormal><span lang=EN-US>Command line arguments:</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- vec <vec_file_name></span></span><span
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lang=EN-US> </span></p>
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<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>name of the
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output file containing the positive samples for training</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- img <image_file_name></span></span><span
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lang=EN-US> </span></p>
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<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>source object
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image (e.g., a company logo)</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- bg <background_file_name></span></span><span
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lang=EN-US> </span></p>
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<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>background
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description file; contains a list of images into which randomly distorted
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versions of the object are pasted for positive sample generation</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- num <number_of_samples></span></span><span
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lang=EN-US> </span></p>
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<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>number of
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positive samples to generate </span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- bgcolor <background_color></span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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lang=EN-US><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> background color (currently grayscale images are assumed); the
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background color denotes the transparent color. Since there might be
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compression artifacts, the amount of color tolerance can be specified by </span><span
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class=Typewch><span lang=EN-US><EFBFBD>bgthresh</span></span><span class=Typewch><span
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lang=EN-US style='font-family:Arial;font-weight:normal'>. </span></span><span
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lang=EN-US>All pixels between </span><span class=Typewch><span lang=EN-US>bgcolor-bgthresh</span></span><span
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lang=EN-US> and </span><span class=Typewch><span lang=EN-US>bgcolor+bgthresh</span></span><span
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lang=EN-US> are regarded as transparent.</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- bgthresh <background_color_threshold></span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- inv</span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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lang=EN-US><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> if specified, the colors will be inverted</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- randinv</span></span><span lang=EN-US> </span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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lang=EN-US><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> if specified, the colors will be inverted randomly</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- maxidev <max_intensity_deviation></span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US><EFBFBD> </span></span><span lang=EN-US>maximal
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intensity deviation of foreground samples pixels</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- maxxangle <max_x_rotation_angle>,</span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- maxyangle <max_y_rotation_angle>,</span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- maxzangle <max_z_rotation_angle></span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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lang=EN-US><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> maximum rotation angles in radians</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>-show</span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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lang=EN-US><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> if specified, each sample will be shown. Pressing <20>Esc<73> will
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continue creation process without samples showing. Useful debugging option.</span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- w <sample_width></span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US><EFBFBD> </span></span><span class=Typewch><span
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lang=EN-US style='font-family:"Times New Roman";font-weight:normal'>width (in
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pixels) of the output samples</span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- h <sample_height></span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US><EFBFBD> </span></span><span class=Typewch><span
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lang=EN-US style='font-family:"Times New Roman";font-weight:normal'>height (in
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pixels) of the output samples</span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US> </span></span></p>
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<p class=MsoNormal><span lang=EN-US>For following procedure is used to create a
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sample object instance:</span></p>
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<p class=MsoNormal><span lang=EN-US>The source image is rotated random around
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all three axes. The chosen angle is limited my</span><span class=Typewch><span
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lang=EN-US> -max?angle</span></span><span lang=EN-US>. Next pixels of
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intensities in the range of </span><span class=Typewch><span lang=EN-US>[bg_color-bg_color_threshold;
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bg_color+bg_color_threshold]</span></span><span lang=EN-US> are regarded as
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transparent. White noise is added to the intensities of the foreground. If </span><span
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class=Typewch><span lang=EN-US><EFBFBD>inv</span></span><span lang=EN-US> key is
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specified then foreground pixel intensities are inverted. If </span><span
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class=Typewch><span lang=EN-US><EFBFBD>randinv</span></span><span lang=EN-US> key is
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specified then it is randomly selected whether for this sample inversion will
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be applied. Finally, the obtained image is placed onto arbitrary background
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from the background description file, resized to the pixel size specified by </span><span
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class=Typewch><span lang=EN-US><EFBFBD>w</span></span><span lang=EN-US> and </span><span
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class=Typewch><span lang=EN-US><EFBFBD>h</span></span><span lang=EN-US> and stored
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into the file specified by the </span><span class=Typewch><span lang=EN-US><EFBFBD>vec</span></span><span
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lang=EN-US> command line parameter.</span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>Positive samples also may be obtained from
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a collection of previously marked up images. This collection is described by
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text file similar to background description file. Each line of this file
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corresponds to collection image. The first element of the line is image file
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name. It is followed by number of object instances. The following numbers are
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the coordinates of bounding rectangles (x, y, width, height).</span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>Example of description file:</span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>Directory structure:</span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>/img</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US><EFBFBD> img1.jpg</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US><EFBFBD> img2.jpg</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>info.dat</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US> </span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US style='font-family:
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||
"Times New Roman";font-weight:normal'>File </span></span><span class=Typewch><span
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lang=EN-US>info.dat:</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>img/img1.jpg<70> 1<> 140
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100 45 45</span></span></p>
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>img/img2.jpg<70> 2<> 100
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200 50 50<35><30> 50 30 25 25</span></span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>Image </span><span class=Typewch><span
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lang=EN-US>img1.jpg</span></span><span lang=EN-US> contains single object
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instance with bounding rectangle (140, 100, 45, 45). Image </span><span
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class=Typewch><span lang=EN-US>img2.jpg</span></span><span lang=EN-US> contains
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two object instances.</span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>In order to create positive samples from
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such collection </span><span class=Typewch><span lang=EN-US><EFBFBD>info</span></span><span
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lang=EN-US> argument should be specified instead of </span><span class=Typewch><span
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lang=EN-US><EFBFBD>img</span></span><span class=Typewch><span style='font-family:"Times New Roman";
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font-weight:normal'>:</span></span></p>
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<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
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class=Typewch><span lang=EN-US>- info <collection_file_name></span></span><span
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lang=EN-US> </span></p>
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<p class=MsoNormal style='margin-left:17.1pt'><span lang=EN-US>description file
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of marked up images collection</span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
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<p class=MsoNormal><span lang=EN-US>The scheme of sample creation in this case
|
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is as follows. The object instances are taken from images. Then they are
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resized to samples size and stored in output file. No distortion is applied, so
|
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the only affecting arguments are </span><span class=Typewch><span lang=EN-US><EFBFBD>w</span></span><span
|
||
lang=EN-US>, </span><span class=Typewch><span lang=EN-US>-h</span></span><span
|
||
lang=EN-US>, </span><span class=Typewch><span lang=EN-US>-show</span></span><span
|
||
lang=EN-US> and </span><span class=Typewch><span lang=EN-US><EFBFBD>num</span></span><span
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class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
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normal'>.</span></span></p>
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<p class=MsoNormal><span lang=EN-US> </span></p>
|
||
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<p class=MsoNormal><span class=Typewch><span lang=EN-US>createsamples</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
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normal'> utility may be used for examining samples stored in positive samples
|
||
file. In order to do this only </span></span><span class=Typewch><span
|
||
lang=EN-US><EFBFBD>vec</span></span><span class=Typewch><span lang=EN-US
|
||
style='font-family:"Times New Roman";font-weight:normal'>, </span></span><span
|
||
class=Typewch><span lang=EN-US><EFBFBD>w</span></span><span class=Typewch><span
|
||
lang=EN-US style='font-family:"Times New Roman";font-weight:normal'> and </span></span><span
|
||
class=Typewch><span lang=EN-US><EFBFBD>h</span></span><span class=Typewch><span
|
||
lang=EN-US style='font-family:"Times New Roman";font-weight:normal'> parameters
|
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should be specified.</span></span></p>
|
||
|
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<p class=MsoNormal><span lang=EN-US> </span></p>
|
||
|
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<p class=MsoNormal><span lang=EN-US>Note that for training, it does not matter
|
||
how positive samples files are generated. So the </span><span class=Typewch><span
|
||
lang=EN-US>createsamples</span></span><span lang=EN-US> utility is only one way
|
||
to collect/create a vector file of positive samples.</span></p>
|
||
|
||
<h2><span lang=EN-US>Training</span></h2>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>The next step after samples creation is
|
||
training of classifier. It is performed by the </span><span class=Typewch><span
|
||
lang=EN-US>haartraining</span></span><span lang=EN-US> utility.</span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US> </span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>Command line arguments:</span><span
|
||
class=Typewch><span lang=EN-US> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- data <dir_name></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> directory name in which the trained classifier is stored</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- vec <vec_file_name></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> file name of positive sample file (created by </span></span><span
|
||
class=Typewch><span lang=EN-US>trainingsamples</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> utility or by any other means)</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- bg <background_file_name></span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> background description file</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- npos <number_of_positive_samples>,</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- nneg <number_of_negative_samples></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> number of positive/negative samples used in training of each
|
||
classifier stage. Reasonable values are npos = 7000 and nneg = 3000.</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- nstages <number_of_stages></span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US><EFBFBD> </span></span><span class=Typewch><span
|
||
lang=EN-US style='font-family:"Times New Roman";font-weight:normal'>number of
|
||
stages to be trained</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- nsplits <number_of_splits></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> determines the weak classifier used in stage classifiers. If </span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman"'>1</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'>, then a simple stump classifier is used, if </span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman"'>2</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> and more, then CART classifier with </span></span><span class=Typewch><span
|
||
lang=EN-US>number_of_splits</span></span><span class=Typewch><span lang=EN-US
|
||
style='font-family:"Times New Roman";font-weight:normal'> internal (split)
|
||
nodes is used</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- mem <memory_in_MB></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> Available memory in MB for precalculation. The more memory you
|
||
have the faster the training process</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- sym (default),</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- nonsym</span></span><span class=Typewch><span
|
||
lang=EN-US style='font-family:"Times New Roman";font-weight:normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> specifies whether the object class under training has vertical
|
||
symmetry or not. Vertical symmetry speeds up training process. For instance,
|
||
frontal faces show off vertical symmetry</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- minhitrate <min_hit_rate></span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> minimal desired hit rate for each stage classifier. Overall hit
|
||
rate may be estimated as </span></span><span class=Typewch><span lang=EN-US>(min_hit_rate^number_of_stages)</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- maxfalsealarm <max_false_alarm_rate></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> maximal desired false alarm rate for each stage classifier. </span></span><span
|
||
class=Typewch><span style='font-family:"Times New Roman";font-weight:normal'>Overall
|
||
false alarm rate may be estimated as</span></span><span class=Typewch><span
|
||
lang=EN-US> (max_false_alarm_rate^number_of_stages)</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- weighttrimming <weight_trimming></span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US><EFBFBD> </span></span><span class=Typewch><span
|
||
lang=EN-US style='font-family:"Times New Roman";font-weight:normal'>Specifies
|
||
whether and how much weight trimming should be used. A decent choice is 0.90.</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- eqw</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- mode <BASIC (default) | CORE | ALL></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> selects the type of haar features set used in training. BASIC use
|
||
only upright features, while ALL uses the full set of upright and 45 degree
|
||
rotated feature set. See [1] for more details.</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- w <sample_width>,</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- h <sample_height></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> Size of training samples (in pixels). Must have exactly the same
|
||
values as used during training samples creation (utility </span></span><span
|
||
class=Typewch><span lang=EN-US>trainingsamples</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'>)</span></span></p>
|
||
|
||
<p class=MsoNormal><span class=Typewch><span lang=EN-US style='font-family:
|
||
"Times New Roman";font-weight:normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal><span class=Typewch><span lang=EN-US style='font-family:
|
||
"Times New Roman";font-weight:normal'>Note: in order to use multiprocessor
|
||
advantage a compiler that supports OpenMP 1.0 standard should be used.</span></span></p>
|
||
|
||
<h2><span lang=EN-US>Application</span></h2>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>OpenCV cvHaarDetectObjects() function (in
|
||
particular haarFaceDetect demo) is used for detection.</span></p>
|
||
|
||
<h3><span lang=EN-US>Test Samples</span></h3>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>In order to evaluate the performance of
|
||
trained classifier a collection of marked up images is needed. When such
|
||
collection is not available test samples may be created from single object
|
||
image by </span><span class=Typewch><span lang=EN-US>createsamples</span></span><span
|
||
lang=EN-US> utility. The scheme of test samples creation in this case is
|
||
similar to training samples creation since each test sample is a background
|
||
image into which a randomly distorted and randomly scaled instance of the
|
||
object picture is pasted at a random position. </span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US> </span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>If both </span><span class=Typewch><span
|
||
lang=EN-US><EFBFBD>img</span></span><span lang=EN-US> and </span><span class=Typewch><span
|
||
lang=EN-US><EFBFBD>info</span></span><span lang=EN-US> arguments are specified then
|
||
test samples will be created by </span><span class=Typewch><span lang=EN-US>createsamples</span></span><span
|
||
lang=EN-US> utility. The sample image is arbitrary distorted as it was
|
||
described below, then it is placed at random location to background image and
|
||
stored. The corresponding description line is added to the file specified by </span><span
|
||
class=Typewch><span lang=EN-US><EFBFBD>info</span></span><span lang=EN-US> argument.</span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US> </span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>The </span><span class=Typewch><span
|
||
lang=EN-US><EFBFBD>w</span></span><span lang=EN-US> and </span><span class=Typewch><span
|
||
lang=EN-US><EFBFBD>h</span></span><span lang=EN-US> keys determine the minimal size of
|
||
placed object picture.</span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US> </span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>The test image file name format is as
|
||
follows:</span></p>
|
||
|
||
<p class=MsoNormal><span class=Typewch><span lang=EN-US>imageOrderNumber_x_y_width_height.jpg</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'>, where </span></span><span class=Typewch><span lang=EN-US>x</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'>, </span></span><span class=Typewch><span lang=EN-US>y</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'>, </span></span><span class=Typewch><span lang=EN-US>width</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> and </span></span><span class=Typewch><span lang=EN-US>height</span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> are the coordinates of placed object bounding rectangle.</span></span></p>
|
||
|
||
<p class=MsoNormal><span class=Typewch><span lang=EN-US style='font-family:
|
||
"Times New Roman";font-weight:normal'>Note that you should use a background
|
||
images set different from the background image set used during training.</span></span></p>
|
||
|
||
<h3><span class=Typewch><span lang=EN-US style='font-family:"Times New Roman"'>Performance
|
||
Evaluation</span></span></h3>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>In order to evaluate the performance of the
|
||
classifier </span><span class=Typewch><span lang=EN-US>performance</span></span><span
|
||
lang=EN-US> utility may be used. It takes a collection of marked up images,
|
||
applies the classifier and outputs the performance, i.e. number of found
|
||
objects, number of missed objects, number of false alarms and other
|
||
information.</span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US> </span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>Command line arguments:</span><span
|
||
class=Typewch><span lang=EN-US> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- data <dir_name></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> directory name in which the trained classifier is stored</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- info <collection_file_name></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> file with test samples description</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- maxSizeDiff <max_size_difference></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'>,</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- maxPosDiff <max_position_difference></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> determine the criterion of reference and detected rectangles
|
||
coincidence. Default values are 1.5 and 0.3 respectively.</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- sf <scale_factor></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'>,</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> detection parameter. Default value is 1.2.</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- w <sample_width>,</span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US>- h <sample_height></span></span><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'> </span></span></p>
|
||
|
||
<p class=MsoNormal style='margin-left:17.1pt;text-indent:-17.1pt'><span
|
||
class=Typewch><span lang=EN-US style='font-family:"Times New Roman";font-weight:
|
||
normal'><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> Size of training samples (in pixels). Must have exactly the same
|
||
values as used during training (utility </span></span><span class=Typewch><span
|
||
lang=EN-US>haartraining</span></span><span class=Typewch><span lang=EN-US
|
||
style='font-family:"Times New Roman";font-weight:normal'>)</span></span></p>
|
||
|
||
<h2><span lang=EN-US>References</span></h2>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>[1] Rainer Lienhart and Jochen Maydt. An
|
||
Extended Set of Haar-like Features for Rapid Object Detection. Submitted to
|
||
ICIP2002.</span></p>
|
||
|
||
<p class=MsoNormal><span lang=EN-US>[2] Alexander Kuranov, Rainer Lienhart, and
|
||
Vadim Pisarevsky. An Empirical Analysis of Boosting Algorithms for Rapid
|
||
Objects With an Extended Set of Haar-like Features. Intel Technical Report
|
||
MRL-TR-July02-01, 2002.</span></p>
|
||
|
||
</div>
|
||
|
||
</body>
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