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