CN103295021A - Method and system for detecting and recognizing feature of vehicle in static image - Google Patents
Method and system for detecting and recognizing feature of vehicle in static image Download PDFInfo
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Abstract
The invention belongs to the field of recognizing types of vehicles through computer image processing, and relates to a method and system for detecting and recognizing a feature and a brand of a vehicle in a static image by means of a digital picture processing technique. A method for detecting the vehicle in the static image is combined with a vehicle logo detecting method which is based on an AdaBoost framework in the method. The method and system for detecting and recognizing the feature of the vehicle in the static images comprises a training part and a detecting part. The training part includes the following steps of manufacturing a vehicle logo sample, collecting an image containing the vehicle logo from the Internet, positioning the vehicle logo, and extracting the vehicle logo image based on position information; calculating a sample feature, constructing 5 different rectangular features with each rectangular feature corresponding to one Haar feature; training a cascade classifier, inputting the training sample acquired from the last step and conducting training, and finally connecting strong classifiers and multiple corresponding weak classifiers obtained in training in series. The detecting part includes the following steps: loading the image to be detected, converting the image into a grey-scale image and conducting histogram equalization, loading the vehicle logo classifiers which include threshold values of the strong classifiers and the weak classifiers and rectangular feature information corresponding to the selected features, conducting cascade vehicle logo detection with the detected image firstly passing the detection of the former strong classifiers. If the detected image is not the vehicle logo image, the detected image can be excluded at the front end, and only the vehicle logo can finally pass the detection of the strong classifiers at various different levels.
Description
Technical Field
The invention relates to a method and a system for detecting and identifying vehicle characteristics in a static picture, belongs to the field of vehicle image identification, and particularly relates to vehicle appearance characteristic detection and vehicle brand identification in the static picture.
Background
With the development of electronic imaging technology and internet, people create pictures, share pictures and obtain pictures more and more conveniently and variously, so that the pictures appearing on the internet are increasing, except for the description of the pictures by characters, a computer does not know the content of the pictures, such as whether a vehicle exists in a certain picture, what characteristics and brand of the vehicle in the picture, and generally, the contents can be known only through human inspection and judgment.
The traditional method for annotating the picture content in the form of keywords cannot be well matched with the corresponding picture, and therefore, the technology that people directly identify the picture content by using a computer according to a certain algorithm is developed. Therefore, even if the internet pictures are not described in advance, the contents of the internet pictures can be known through the image recognition technology, wherein the contents comprise the detection and the recognition of the vehicle.
The existing vehicle characteristic detection and identification technology is mainly applied to videos, such as intelligent traffic monitoring, and moving vehicles can be easily detected through multi-frame information of the videos so as to be positioned and identified; however, these conventional methods are not suitable for use in still pictures, and a method for detecting and identifying vehicles in still pictures is sought. With the development of electronic commerce, more and more commodities are directly facing consumers on the network, including vehicles. If the computer can know whether a vehicle exists in a certain picture in the Internet and know the brand of the vehicle, the popularization of the vehicle by a vehicle seller is facilitated, so that the technology for detecting and identifying the vehicle in the static picture has wide application prospect.
Disclosure of Invention
The invention aims to provide a method for detecting and identifying vehicle characteristics by using static pictures, and provides a system for detecting and identifying vehicle characteristics by using internet pictures, which comprises the following steps: the method comprises the steps of carrying out vehicle detection on a network picture to be detected, judging the posture of a vehicle, carrying out vehicle logo detection in the front area or the rear area of the vehicle so as to identify the vehicle, and the flow is shown in fig. 1.
The invention provides a vehicle detection method in a static picture, which comprises two parts of training and detection.
The training phase comprises the following steps:
(1) manufacturing a vehicle sample: normalizing the input picture, namely performing gamma normalization on each color component of the input image to adapt to the condition that the image is too dark or the contrast is low, wherein the operation adopted by the method is to perform logarithm operation on the color components;
(2) calculating sample characteristics: calculating a Histogram of Oriented Gradient (HOG) feature pyramid of the normalized image;
(3) training a vehicle model: and (3) transmitting the characteristic data set of the sample into an implicit Support Vector machine (LSVM) of a training classifier for learning. A hybrid model of a root model, a component model, and a corresponding deformable component model of the vehicle is generated by learning.
The detection phase comprises the following steps:
(1) loading a picture to be tested: normalizing the input picture, namely performing gamma normalization on each color component of the input image;
(2) and (3) feature calculation: calculating an HOG characteristic pyramid of the network picture to be detected;
(3) loading a vehicle model: loading a data file storing a vehicle model;
(4) vehicle detection: and scanning an area matched with the variable component model on the characteristic pyramid through a multi-scale detection algorithm of a scale space to realize the detection and positioning of the vehicle.
The invention provides a static picture vehicle identification method, in particular to a vehicle logo detection method based on an AdaBoost framework, which comprises two parts of training and detection.
The training phase comprises the following steps:
(1) manufacturing a car logo sample;
(2) calculating sample characteristics;
(3) and training a cascade classifier.
The detection phase comprises the following steps:
(1) loading a picture to be tested;
(2) loading a car logo classifier;
(3) and detecting the cascade vehicle logo.
Drawings
FIG. 1 is a flow diagram of still-picture vehicle feature detection and identification.
Fig. 2 is a schematic composition diagram of a model-deformable part of a training vehicle model 6.
FIG. 3 is a schematic illustration of target deforming member positioning in vehicle feature detection.
FIG. 4 is a schematic diagram of Haar features for sample feature computation.
Fig. 5 is a schematic flow chart of cascade logo detection.
Fig. 6 is a diagram illustrating the result of the cascade emblem detection process shown in fig. 5.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.
The existing vehicle detection method only builds a model for the front side of the vehicle, when the vehicle in the picture appears on the side surface or the rear surface, the vehicle is probably not detected, and the existing method solves the problem that the model can only be built for each side surface of the vehicle, so that the calculated amount is increased, and the shielding problem cannot be well avoided. The method of the invention is to respectively establish models (root models) at the front, the back and the side of the vehicle, divide each model into different parts, respectively establish models (part models and deformable part models) for each part and corresponding deformation thereof, and finally fuse the models into a final vehicle model.
The existing vehicle identification method is to locate the vehicle logo of the vehicle by locating the vehicle license plate, and then distinguish the brand of the vehicle by the edge characteristics of the image of the vehicle logo, and the like, and the method depends on the accuracy of license plate location, but many network vehicle pictures have no license plate, such as the vehicle pictures on the vehicle exhibition; in addition, the accuracy, such as illumination and noise, can also be reduced due to the influence of the environment by simply judging various types of car logos through the features. According to the method, on the basis of extracting Haar features, a large number of car logo samples are trained by an AdaBoost method to obtain a cascade classifier for detecting the car logos in the pictures to be detected, and the method can directly position the car logos in the pictures and is not influenced by the environment.
The invention provides a vehicle detection method in a static picture, which comprises two parts of training and detection and is characterized in that:
the training phase comprises the following steps:
(1) manufacturing a vehicle sample:
the gamma normalization is carried out on each color component of the input image so as to adapt to the condition that the image is too dark or the contrast is low. The sample pictures are used for marking the vehicles in the sample pictures by rectangles in advance.
(2) Calculating sample characteristics:
and constructing an image pyramid for the normalized sample image, scanning by windows with certain sizes on each layer, and calculating HOG characteristics in each window to form a characteristic pyramid.
(3) Training a vehicle model:
in order to adapt to different postures and shielding problems of vehicles, 2 template models are established for vehicle models, wherein one template model is a model of a front view angle and a rear view angle of the vehicle (the front view angle and the rear view angle of the vehicle are not greatly different and are represented by one share), and the other template model is a vehicle side model; for each template model, the invention divides it into 6 parts, as shown in fig. 2.
The vehicle model of the present invention contains n =2 sub-templates, using (n + 2) tuples
Is defined in which
Representing a root filter (HOG feature model),
is the model of the ith sub-template, and b is the offset. Each sub-template contains m =6 part models, with m
The triple definition, wherein,
is a filter of the jth component, of size
，
The fixed position of the j-part relative to the root,is a loss function of the sub-template from the correct position in the vehicle model.
In the image feature pyramid, each filter is located at a position of
Wherein
Indicating that the ith filter is in the characteristic pyramid
The position of the layer, the corresponding response is the sum of the responses of each filter at the respective position minus the vehicle component variation loss, plus the offset:
wherein, H represents a characteristic pyramid,
indicates juxtaposition in H in row-priority order
In the upper left corner
The feature vectors of the sub-windows are,
the offset of the ith component from the fixed position,
is a deformation feature.
The model is simplified to be combined with a classifier to obtain model parameters:
using m-element arrays
Defining a mixture model comprising m sets of models, wherein
Is an object model of group c, wherein. Single object model in hybrid model
At the position of each filter of
Wherein
Is a model
Number of middle partsSimplifying the filter positions of the c-th group of models to
. Model parameter vectors for position response of hybrid models
Sum vector
The dot product of (d). Wherein the vector
Is a concatenation of model parameter vectors in each single target model,
(ii) a And vector
Appearing sparse. Different models of the same target can be obtained by adopting a plurality of groups of models to learn, so that a mixed model is combined.
And the LSVM is adopted to learn the component model, so that hidden information in the image can be fully utilized, and the model can be enriched.
The detection phase comprises the following steps:
(1) loading a picture to be tested:
the input picture is normalized, i.e. gamma normalization is performed for each color component of the input image.
(2) And (3) feature calculation:
and constructing an image pyramid for the normalized image to be detected, scanning by windows with certain sizes on each layer, and calculating HOG characteristics in each window to form a characteristic pyramid.
(3) Loading a vehicle model:
a data file storing a vehicle model is loaded.
(4) Vehicle detection:
computing a stored response of the feature pyramid first layer features with the first model filter,and (3) performing distance conversion on the component filter:
the filter response at the nearby position is expanded by using distance conversion, and the deformation loss of the component is added, so that the detection precision is improved.
Is the maximum distance of the ith part from the root position where the root position filter response is placed at the ith layer (x, y) for that part.
The response for each layer root position is calculated using the sum of the corresponding layer root filter responses plus the transformed and sampled component filter:
whereinThe total number of layers of the feature pyramid.
Calculate the optimal offset of the part:
by root position
At an optimum offset
The corresponding part position is searched, and the positioning of the target deformation part is realized, and the result is shown in figure 3.
Secondly, the method for detecting the car logo based on the AdaBoost frame comprises two parts of training and detecting, wherein the steps of the training stage are as follows:
(1) manufacturing a car logo sample:
the method comprises the steps of collecting pictures containing car logos from a network, calibrating the positions of the car logos, extracting car logo images according to position information, zooming according to the inherent length-width ratio of the car logos, eliminating illumination influences through histogram equalization to serve as a positive sample of the car logos, and taking other parts of the car pictures without the car logos as negative samples.
(2) Calculating sample characteristics:
5 different rectangular features are constructed, each rectangular feature corresponds to a Haar feature (shown in FIG. 4), the Haar feature is defined as the sum of weighted values of the sum of pixel values of the corresponding rectangular region, and the Haar feature is calculated by means of integral images.
The integral image is defined as:
Integral image
Calculating in an incremental manner:
provision for
And then, the integral image can be calculated by traversing the whole image once according to the rows or the columns. The sum of the pixel values of a certain rectangular area in the original image is calculated, and as long as four values are obtained by inquiring the integral image according to the positions of four vertexes of the rectangle, certain addition and subtraction operation of the four values can be equivalent to the sum of the pixel values of the rectangular area.
And (3) Haar feature calculation:
the first feature is that:
wherein
Is the sum of the pixel values of the white rectangular area,
is the sum of the pixel values of the black rectangular area.
The second characteristic:
a third feature:
a fourth feature:
the fifth characteristic:
features are computed on n samples of the input, including m positive samples and n-m negative samples, each sample having two attributes
Where x represents the Haar feature vector of the sampleY represents the class of the sample, and is taken as 1 for positive samples and-1 for negative samples.
(3) Training a cascade classifier:
inputting n training samples obtained in the previous step:
defining a weak classifier:
is a sample
The Haar feature vector of the image,
is an unequal number direction controller, takes a value of +1 or-1,
a threshold is trained for the weak classifier.
Initializing error weights:
in the first placeIn the secondary training:
wherein
Is set total training times;
normalization weight:
for each featureGenerate its corresponding weak classifier
Calculating the error with respect to the current weight:
selecting the one with the smallest error
Weak classifier of
Adding the feature information into a strong classifier, recording rectangular feature information corresponding to the feature at the moment, and updating the weight:
wherein, if it is
A sample
Is correctly classified, then
Otherwise
；
Forming a final strong classifier:
wherein,
under the set training times T, a strong classifier is generated in each training, a plurality of weak classifiers are selected in the training process, and finally each strong classifier and the corresponding weak classifiers are connected in series to form the final cascade classifier.
The detection stage comprises the following steps:
(1) loading a picture to be tested: converting the image into a gray scale image and carrying out histogram equalization;
(2) loading a car logo classifier: the classifier data obtained by training is stored in a corresponding txt file, wherein the data structure is described as:
n strong classifiers of
Each strong classifier includes a threshold of the strong classifierAnda weak classifier;
wherein, the firstjEach weak classifier includes a threshold of the weak classifier
Direction controller
Coefficient of
；
And rectangular feature information corresponding to the features selected by the weak classifier: number of rectangles
Type of rectangular featurePosition information and weight of each sub-rectangle
(3) Detecting a cascade vehicle logo: in the cascade classifier, the strong classifier is more complex than one stage, the detection image firstly passes through the detection of the previous strong classifier, if the detection image is not the car logo image, the detection image is eliminated at the front end, only the car logo image can finally pass through the detection of the strong classifier at each stage, a large number of non-car logo images are eliminated when the strong classifier at the previous stages is detected, the process is as shown in fig. 5, and the result is as shown in fig. 6.
Claims (12)
1. The invention aims to provide a method for detecting and identifying vehicle characteristics by using static pictures, and provides a system for detecting and identifying vehicle characteristics by using internet pictures, which comprises the following steps: and carrying out vehicle detection on the network picture to be detected, judging the posture of the vehicle, and carrying out vehicle logo detection in the front area or the rear area of the vehicle so as to identify the vehicle.
2. The invention provides a vehicle detection method in a static picture, which comprises two parts of training and detection.
3. The training phase comprises the following steps: by manufacturing a vehicle sample, normalizing an input picture; then, sample feature calculation is carried out, and an image pyramid is constructed on the normalized image; next, training a vehicle model, and transmitting the characteristic data set of the sample into a training classifier for learning; generating a hybrid model of a root model, a component model, and a corresponding deformable component model of the vehicle by learning; the detection stage comprises: loading a picture to be tested: normalizing the input picture, namely performing gamma normalization on each color component of the input image; and (3) carrying out feature calculation: constructing an image pyramid for the normalized image to be detected; loading a vehicle model, and loading a data file for storing the vehicle model; and finally, vehicle detection is carried out, and an area matched with the variable component model is scanned on the characteristic pyramid to realize the detection and positioning of the vehicle.
4. The invention provides a car logo detection method based on an AdaBoost framework, which comprises two parts of training and detection.
5. The training phase comprises the following steps: making a car logo sample, acquiring a picture containing a car logo from a network, calibrating the position of the car logo, and extracting a car logo image according to position information; calculating sample characteristics, and constructing rectangular characteristics, wherein each rectangular characteristic corresponds to a Haar characteristic; training the cascade classifier, inputting the training sample obtained in the last step and training, and finally connecting the strong classification obtained by training and a plurality of corresponding weak classifiers in series; the detection stage comprises: loading a picture to be tested: converting the image into a gray scale image and carrying out histogram equalization; loading a vehicle logo classifier which comprises thresholds of a strong classifier and a weak classifier and rectangular feature information corresponding to the selected features; and (3) detecting the cascade vehicle logo, wherein the detection image firstly passes through the detection of the strong classifiers in the front, if the detection image is not the vehicle logo image, the detection image is eliminated at the front end, and only the vehicle logo image can finally pass through the detection of the strong classifiers in each stage.
6. The method for making a vehicle sample during a training phase and loading a picture to be tested during a testing phase as claimed in claim 1, wherein: the gamma normalization is carried out on each color component of the input image so as to adapt to the condition that the image is too dark or the contrast is low, the logarithm operation is carried out on the color components, and the vehicle in the sample image is marked by a rectangle in advance.
7. The method of training phase sample feature computation of claim 2, wherein: and scanning the normalized sample on each layer by using a window with a certain size, and calculating the HOG characteristic in each window to form a characteristic pyramid.
8. A method of training a vehicle model during a training phase as claimed in claim 3, wherein: in order to adapt to different postures and shielding problems of vehicles, 2 template models are established for vehicle models, wherein one model is a model of a front view angle and a rear view angle of the vehicle, and the other model is a vehicle side model.
9. The method of inspection phase vehicle inspection of claim 4, wherein: computing a characteristic pyramidlLayer characteristics andithe storage response of each model filter, the distance conversion is carried out on the component filter, the filter response of the nearby position is expanded by utilizing the distance conversion, the deformation loss of the component is added, and the detection precision is improved;D i,j (x,y)is the firstiMaximum distance of each part from root position, wherein root position filter response is placed at the second part corresponding to the partlLayer (A)x,y) Calculating a response for each layer root position using the sum of the corresponding layer root filter responses plus the transformed and sampled component filter; then, the optimal offset of the component is calculated, using the root positionx 0, y 0, l 0) And searching the corresponding part position in the optimal offset to realize the positioning of the target deformation part.
10. A method of making emblem samples for a training phase based on the AdaBoost framework of claim 5, wherein: and extracting the car logo image according to the position information, zooming according to the inherent length-width ratio of the car logo, eliminating the illumination influence through histogram equalization, and taking the car logo image as a positive sample and other parts without the car logo as negative samples.
11. The method of AdaBoost framework-based training phase sample feature computation of claim 6, wherein: defining the Haar characteristics as the sum of weighted values of the sum of pixel values of the corresponding rectangular area, and calculating the Haar characteristics in an integral image mode; integral image SAT (x, y) Representing pixels in the original image (x, y) The sum of all pixel values at the upper left part is calculated in an incremental mode, and then the corresponding integral image can be calculated only by traversing the whole image once according to rows or columns; the sum of the pixel values of a certain rectangular area in the original image is calculated, and as long as four values are obtained by inquiring the integral image according to the positions of four vertexes of the rectangle, certain addition and subtraction operation of the four values can be equivalent to the sum of the pixel values of the rectangular area.
12. The method of training a cascade classifier based on an AdaBoost framework training phase of claim 7, wherein: under the set training times T, a strong classifier is generated in each training, a plurality of weak classifiers are selected in the process, and finally each strong classifier and the corresponding weak classifiers are connected in series to form the final cascade classifier.
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