CN111259843B - Multimedia navigator testing method based on visual stability feature classification registration - Google Patents
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Abstract
A multimedia navigator testing method based on visual stability characteristic classification registration relates to a multimedia navigator testing method, in particular to a multimedia navigator testing method based on visual stability characteristic classification registration. The invention aims to solve the problem that image detail information cannot be captured due to over-bright background in the multimedia navigation display interface testing process. The method comprises the following specific steps: step one, preparing a temporary sample library in advance, carrying out primary sample collection on a target, naming pictures according to a corresponding sequence and storing the pictures; secondly, SURF characteristic point detection is carried out on all collected samples; step three, randomly extracting part of feature point sets; step four, finishing the training phase; step five, adopting a mode of voting for multiple scenes; step six, performing fast nearest neighbor approximate search algorithm matching on all feature points in the currently acquired image and all feature points of the matched image in the sample library; and step seven, continuously framing by the camera according to a certain frame rate. The invention belongs to the technical field of communication.
Description
Technical Field
The invention relates to a multimedia navigator testing method, in particular to a multimedia navigator testing method based on visual stability characteristic classification registration, and belongs to the technical field of communication.
Background
The development of computer technology enables the production and life of human beings to continuously develop towards the direction of intellectualization and visualization, computer vision is also more and more widely applied to various fields of industrial production, and machines can have human-like vision systems and process complex tasks more intelligently and flexibly. The digital image processing technology is one of the realization methods of computer vision, but for the traditional image processing algorithm, the influence of light level change and noise interference on the image analysis result is great, and the digital image processing method has no strong anti-interference capability; for example, in the background of the patent, for a scene or an object with self-luminous capability, it is difficult to maintain high adaptability to all targets and tasks by using one algorithm, and especially, online real-time processing is poor in effect due to various uncertain factors. Even if slight changes in light may not appear to be very different to the human eye, each pixel value is increased by an order of magnitude from a pixel point of view analysis, and globally the average level of the image changes significantly. Such variations will be decisive for the final processing result. Based on this, the stability of the image processing algorithm starting from the global situation is poor, and the adaptability to different environments is difficult to meet the actual requirement. However, in consideration of local characteristics, such random disturbance or change in a controllable range always causes difficulty in annihilation of local stable characteristics, and therefore, a design method of extracting and analyzing local characteristic information is mainly adopted in the present design.
The image registration is a technology capable of realizing retrieval and identification of unknown scenes, and is an important technical means for realizing tasks such as visual navigation, image information retrieval and the like. Generally, the core of registration is the detection and matching of features, such as SIFT, SURF, and other feature descriptors, which can extract the local key feature information of an image under the condition of large interference, and the registration still has good effect even under a complex environment. Feature detection is a fundamental algorithm in computer vision and image processing, which uses a computer algorithm to process image information, determines from a digital image perspective whether a pixel point of each image belongs to a feature of the image, or divides the points on the image into different subsets, and determines whether it belongs to an isolated point, a continuous curve, or a continuous region. It features that local analysis is performed from some positions in image, rather than whole image, and a certain local feature is stable enough to be judged as a key point of image. The method is widely applied to the fields of target recognition, image registration, visual tracking, three-dimensional reconstruction and the like.
As described above, one of the difficulties in detecting such a luminous target related to this patent is the change of brightness, which is determined by the ambient light level and does not change greatly in a short time, unlike the general target that can only reflect external light; however, the self-luminous target may be darker or brighter, or may be alternately bright and dark at any time, so that the quality of the acquired image is uneven, the possible brightness taken at a certain moment is appropriate, but the acquired image at the next moment is an overexposed image; one of the solutions is to automatically adjust the exposure time, but frequent adjustment in a short time is a great challenge for the camera, and especially for long-term application in industrial production, the camera has a great influence on the system reliability, and at the same time, the control flow is more complicated. Based on the above factors, we consider adjusting the camera exposure to a lower level, ensuring that the imaging at the maximum brightness of the target can still maintain a higher quality, and ensuring local key information even though global quality is lost at the lower brightness. On the basis, the specific position of the luminous object in the dark light environment can be restored by calculating the homography matrix according to the relative position relation of the matching key points in the real-time image and the sample image, and even people without judgment experience are difficult to position the target through the image.
The characteristic point detection mainly comprises the processes of extracting local characteristic points, describing the characteristic points, matching the characteristic points and the like. The well-known Feature detection algorithms which are applied more currently include a Scale-Invariant Feature Transform (SIFT-SIFT) Feature, namely, a Scale-Invariant Feature Transform, and a Speeded-Up Robust Feature. The two well realize the task of extracting the characteristics from the image, the LoG is approximated by the DoG in the former, a scale space is established to search for local extreme points and correct, each characteristic is described by a 128-dimensional vector, and the processing of the details of the characteristic points is very detailed; the latter is optimized to have more advantage in algorithm speed, which is much faster than SIFT, but is generally processed by a stable degree in scale and rotation invariance of images. Compared with SIFT characteristics, the method greatly improves the execution efficiency of the program, reduces the dimension of the description of the key points except for using a Hession matrix, and only adopts 64-dimension vectors to describe the characteristics. Feature vectors, also called feature descriptors, are used to store information about the keypoints themselves and their surrounding neighbors. The description of a feature point not only contains its own information in isolation, but also should embody a complete description of a local range, each feature point will form a multi-dimensional feature vector, and the feature vector describes the feature and all the surrounding information in detail.
In order to improve the efficiency of a control algorithm and further improve the detection and identification speed, and compare the performances of different algorithms through experiments, the SURF algorithm is adopted, meanwhile, the algorithm is improved for improving the detection precision and accuracy of the system, and a traditional method for carrying out violent matching on key points or directly matching and identifying the key points by a fast nearest neighbor approximate search algorithm is not adopted. The traditional feature point matching requires that after sampling each time, the extracted feature points are respectively subjected to one-to-one matching on N pictures in a sample library, and a certain sample with the optimal matching is taken as a recognition result, but the time overhead in the mode becomes very large along with the increase of the number of scene types, and the requirement of online real-time detection can be hardly met. The other idea is to perform classification learning on all feature points according to different label scenes, train an SVM decision maker with excellent performance aiming at the classification task by sample data features, rapidly classify the feature points of the collected new scene or target, and judge whether the current scene exists in a scene library or not, if so, the best matching is returned. The Support Vector Machine (SVM) mentioned here is a generalized linear classifier for binary classification of data by supervised machine learning, whose decision boundary is the maximum margin segmentation hyperplane solved for the learning samples. The core idea of the method is to linearly classify samples to be classified, although most tasks are linearly inseparable in an original space, nonlinear classification effects can be obtained by mapping input data to a high-dimensional space by using a nonlinear function and then applying a hyperplane selected by a linear SVM. SVMs were originally proposed for the two-classification problem, i.e., only when there are two possible scenarios, the traditional SVM can function. How to effectively popularize the method to multi-class classification is one of important contents of current research on support vector machines. The currently proposed solutions include one-to-one (one-against-one) and one-to-many (one-against-rest) methods, but both the classifier performance and the existence of non-partitioned areas are problems to be solved. The general SVM classifier has the obvious disadvantage that the distribution condition of each class is not considered, the general SVM classifier has obvious tendency in processing data with unbalanced sample distribution, for example, the number of detected key points in scene 1 is far more than that in scene 2, and the trained classifier is more favorable for scene 1 in decision making.
Therefore, aiming at the problem of stable feature classification and registration designed by the patent, a complete binary tree SVM multi-classification algorithm based on a spherical structure is used for training and learning feature points of all samples uniformly, classification is carried out according to the distribution characteristics of feature descriptors in a hyperspace, and an excellent feature classifier is learned according to the classification.
Disclosure of Invention
The invention provides a multimedia navigator test method based on visual stability characteristic classification registration, which aims to solve the problem that image detail information cannot be captured due to over-bright background in the multimedia navigation display interface test process.
The technical scheme adopted by the invention for solving the problems is as follows: the method comprises the following specific steps:
step one, preparing a temporary sample library in advance, carrying out primary sample collection on a target, naming pictures according to a corresponding sequence and storing the pictures;
secondly, SURF characteristic point detection is carried out on all collected samples;
randomly extracting part of feature point sets, training by applying a supervised machine learning classification algorithm, namely a complete binary tree SVM (support vector machine) based on a spherical structure, finding out a proper decision function, and adjusting learning strategies and parameters by using other feature points to continuously optimize until the system can accurately classify the feature point sets into N types of scenes matched with scene labels;
step four, finishing the training phase, wherein the camera can start to collect images, preprocessing the images, delivering the images to a feature extractor, keeping the images consistent with detection parameters of a training classifier, and acquiring a plurality of local invariant feature point information;
step five, a multi-scene voting mode is adopted, if the feature point Ki belongs to the interface Xi, the number of votes in the category is increased by 1, the same operation is completed on all the feature points, and the scene category with the largest number of votes is regarded as the best registration result with the current interface;
step six, performing fast nearest neighbor approximate search algorithm matching on all feature points in the currently acquired image and all feature points of the matched image in the sample library to find out the best matching feature point pair, calculating a homography matrix by using a random sampling consensus algorithm, wherein the homography matrix describes the transformation relation of two positions of a target in an image two-dimensional space, and detecting the position of the navigator in the unknown scene image;
and step seven, continuously framing according to a certain frame rate by the camera, detecting and outputting a registration result in real time by the system along with the change of the content of the liquid crystal screen, and performing cumulative recording on the detection result by the background and storing the detection result to the local in a log form.
Furthermore, when the display interfaces corresponding to all keys on the multimedia navigator are shot in the step one, the stability of the system is ensured because the subsequent algorithm designs margins aiming at the changes of the environment, the platform installation and the like, and if no special condition exists, the step only needs to be carried out once.
Furthermore, in the second step, during the SURF feature point detection of all the collected samples, it is required to ensure that the detection parameters are consistent, and each sample is required to be capable of extracting at least n feature points.
Further, assuming that there are N scene categories, k is extracted under a certain sceneiN, where i is 1,2,3iA set of feature points, where kiN, each feature point is described by a 64-dimensional vector.
Further, when the screen registration result does not conform to the expectation in step seven, the system automatically records the current time and stores the frame of image, so as to facilitate further analysis manually.
The invention has the beneficial effects that: the invention can complete a set of automatic test flow of operation and detection aiming at multimedia navigators of different models. The traditional navigator testing link needs to depend on a manual operation mode on a production line, the workload is high, the efficiency is low, and the phenomena of missing detection and false detection easily occur under a long-time condition. And the automation mode of computer vision can completely replace manual work, and the whole process completely does not need human participation by depending on the multimedia intelligent test platform in the design, thereby greatly improving the test efficiency and being suitable for large-batch online detection.
The traditional detection and identification of the luminous object are mostly completed by deep learning based on the neural network due to the complexity, so that a large number of samples are needed, the training period is long, the hardware has high requirements, the cost is high, the expansibility is poor, and retraining and designing software are needed when label scenes are added or different types of navigators are replaced. The characteristic classification registration technology provided by the patent greatly improves the stability and practicability of the platform on the premise of guaranteeing the beauty of the basic functions of realizing the multimedia test. A large number of experiments prove that the stability and the accuracy of the system can be still maintained under different lighting conditions, different positions and different installation modes, and even under the condition of artificially adding external interference (such as partial shielding or strong light to a target).
In order to enhance the adaptability of the design, not only are various jigs matched with the mechanical arm for operation prepared at the tail end of the mechanical arm, but also the expansibility of software is considered, when new interface content is replaced or added, the new interface is only required to be sampled and added into a sample library, and a label is defined, so that the navigators with different models and different scenes can be tested, redesign is not required, the practical application is facilitated, and the applicability is wide. Meanwhile, the design also provides a feasible scheme for identifying and positioning the target under the dark light condition. In conclusion, the detection platform has the significance of adopting an intelligent mode based on machine vision to detect the key functions of the multimedia navigator, and can also be used for identifying and positioning all luminous objects, so that the production efficiency of enterprises is improved, and the competitiveness of the enterprises is developed.
Drawings
FIG. 1 is a block diagram of the algorithm flow of the present invention.
Detailed Description
The first embodiment is as follows: the embodiment is described with reference to fig. 1, and the specific steps of the method for testing a multimedia navigator based on classification and registration of visual stability features in the embodiment are as follows:
step one, preparing a temporary sample library in advance, carrying out primary sample collection on a target, naming pictures according to a corresponding sequence and storing the pictures;
secondly, SURF characteristic point detection is carried out on all collected samples;
randomly extracting part of feature point sets, training by applying a supervised machine learning classification algorithm, namely a complete binary tree SVM (support vector machine) based on a spherical structure, finding out a proper decision function, and adjusting learning strategies and parameters by using other feature points to continuously optimize until the system can accurately classify the feature point sets into N types of scenes matched with scene labels;
step four, finishing the training phase, wherein the camera can start to collect images, preprocessing the images, delivering the images to a feature extractor, keeping the images consistent with detection parameters of a training classifier, and acquiring a plurality of local invariant feature point information;
step five, a multi-scene voting mode is adopted, if the feature point Ki belongs to the interface Xi, the number of votes in the category is increased by 1, the same operation is completed on all the feature points, and the scene category with the largest number of votes is regarded as the best registration result with the current interface;
step six, performing fast nearest neighbor approximate search algorithm matching on all feature points in the currently acquired image and all feature points of the matched image in the sample library to find out the best matching feature point pair, calculating a homography matrix by using a random sampling consensus algorithm, wherein the homography matrix describes the transformation relation of two positions of a target in an image two-dimensional space, and detecting the position of the navigator in the unknown scene image;
and step seven, continuously framing according to a certain frame rate by the camera, detecting and outputting a registration result in real time by the system along with the change of the content of the liquid crystal screen, and performing cumulative recording on the detection result by the background and storing the detection result to the local in a log form.
The second embodiment is as follows: the embodiment is described with reference to fig. 1, in a first step of the method for testing a multimedia navigator based on visual stability feature classification registration in the embodiment, when a display interface corresponding to all keys on the multimedia navigator is photographed, because a subsequent algorithm has designed margins for changes of environments, platform installation and other conditions to ensure the stability of the system, if no special condition exists, the step is performed only once.
The third concrete implementation mode: referring to fig. 1, the embodiment is described, and in the second step of the method for testing a multimedia navigator based on visual stability feature classification and registration in the embodiment, detection parameters should be ensured to be consistent in the SURF feature point detection process performed on all collected samples, and each sample is required to be capable of extracting at least n feature points.
The fourth concrete implementation mode: the embodiment is described with reference to fig. 1, N scene categories exist in the assumption of the method for testing a multimedia navigator based on classification and registration of visual stability features in the embodiment, and k is extracted in a certain sceneiN, where i is 1,2,3iA set of feature points, where ki>n, each feature point is described by a 64-dimensional vector.
The fifth concrete implementation mode: in the seventh step of the method for testing a multimedia navigator based on classification and registration of visual stability features according to the embodiment, when the screen registration result does not match the expected result, the system automatically records the current time and stores the frame of image, which is convenient for further analysis manually.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. The multimedia navigator testing method based on the visual stability feature classification registration is characterized by comprising the following steps of: the multimedia navigator testing method based on the visual stability feature classification registration specifically comprises the following steps:
step one, preparing a temporary sample library in advance, carrying out primary sample collection on a target, naming pictures according to a corresponding sequence and storing the pictures;
secondly, SURF characteristic point detection is carried out on all collected samples; the consistency of detection parameters is ensured in the process of SURF characteristic point detection on all collected samples, each sample is required to be capable of extracting at least N characteristic points and N scene categories, and k is extracted under each sceneiN, wherein i is 1,2,3iA set of feature points, where kiN, each feature point is described by a 64-dimensional vector;
randomly extracting part of feature point sets, training by applying a supervised machine learning classification algorithm, namely a complete binary tree SVM (support vector machine) based on a spherical structure, finding a decision function, and adjusting learning strategies and parameters by using other feature points to continuously optimize until the system can accurately classify the feature point sets into N types of scenes matched with scene labels;
step four, finishing the training phase, starting to acquire images by a camera, preprocessing the images, delivering the images to a feature extractor, keeping the images consistent with detection parameters of a training classifier, and acquiring a plurality of local invariant feature point information;
step five, adopting a mode of voting for multiple scenes, and if the feature point belongs to the interface XiAdding 1 to the ticket number of the category, completing the same operation on all the feature points, and considering the scene category with the maximum ticket number as the optimal registration result with the current interface;
step six, performing fast nearest neighbor approximate search algorithm matching on all feature points in the currently acquired image and all feature points of the matched image in the sample library to find out the best matched feature point pair, calculating a homography matrix by a random sampling consensus algorithm, wherein the homography matrix describes the transformation relation of the two times of positions of the target in the two-dimensional space of the image, and detecting the position of the navigator in the unknown scene image;
and step seven, the camera continuously frames according to the inherent frame rate of the camera, the system detects and outputs a registration result in real time along with the change of the content of the liquid crystal screen, and the background carries out cumulative record on the detection result and stores the result to the local in a log mode.
2. The method for testing the multimedia navigator based on the classification and registration of the visual stability characteristics as claimed in claim 1, wherein: in the first step, when the display interfaces corresponding to all keys on the multimedia navigator are shot, the stability of the system is ensured because the subsequent algorithm designs allowance aiming at the change of the environment and the installation condition of the platform, and the step only needs to be carried out once.
3. The method for testing the multimedia navigator based on the classification and registration of the visual stability characteristics as claimed in claim 1, wherein: and seventhly, when the screen registration result does not accord with the prediction, the system automatically records the current time and stores the frame of image, so that the further analysis is facilitated manually.
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