Patent Publication Number: US-2019197349-A1

Title: Image identification method and image identification device

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an image identification method and a related image identification device, and more particularly, to an image identification method of preventing identification accuracy from being affected by illumination change and a related image identification device. 
     2. Description of the Prior Art 
     Video content analyzing technology can be applied to a monitoring apparatus and used to detect a moving object inside a monitoring region of the monitoring apparatus for increasing image monitoring efficiency and safety. The video content analyzing technology is easily affected by illumination change when executing motion detection, and the illumination change may be resulted from sunlight, vehicle light, street light and a shadow of the object. The illumination change may result in variation of pixel value about the monitoring image, which is not an aim of the video content analyzing technology, thus a result of the video content analyzing technology has noise and accuracy of the video content analyzing technology is decreased accordingly. 
     The conventional video content analyzing technology has to spend large computation and a long period to detect and analyze the illumination change of the monitoring image, and cannot immediately acquire a result of detecting and filtering the illumination change. Therefore, conventional computation of detecting and filtering the illumination change is executed by a backend server, which has preferred computational ability, but cannot be executed by the monitoring camera with limited computational ability. 
     SUMMARY OF THE INVENTION 
     The present invention provides an image identification method of preventing identification accuracy from being affected by illumination change and a related image identification device for solving above drawbacks. 
     According to the claimed invention, an image identification method of preventing identification accuracy from being affected by illumination change includes acquiring a first monitoring image and a second monitoring image respectively captured before and after the illumination change, dividing the first monitoring image and the second monitoring image respectively into a plurality of areas, computing pixel difference between each area of the second monitoring image and a corresponding area of the first monitoring image, classifying the pixel difference corresponding to the plurality of areas into at least one group, and determining whether an area related to the at least one group is filtered according to distributed concentration of the at least one group. 
     According to the claimed invention, an image identification device with a function of preventing identification accuracy from being affected by illumination change is disclosed. The image identification device includes an image receiver and an operation processor. The image receiver is adapted to receive a plurality of monitoring images. The operation processor is electrically connected with the image receiver and adapted to acquire a first monitoring image and a second monitoring image respectively captured before and after the illumination change, divide the first monitoring image and the second monitoring image respectively into a plurality of areas, compute pixel difference between each area of the second monitoring image and a corresponding area of the first monitoring image, classify the pixel difference corresponding to the plurality of areas into at least one group, and determine whether an area related to the at least one group is filtered according to distributed concentration of the at least one group, for excluding some area within the monitoring image having the illumination change but without foreground variation. 
     The image identification method and the image identification device of the present invention can compute the pixel difference between areas from different monitoring images captured before and after the illumination change, and classify the pixel difference of all areas to determine whether one of the groups has the greater distributed concentration. The area related to the group having the greater distributed concentration (which means conforms to a specific condition) can be represented as the interfered area affected by the illumination change and having equivalent pixel variation inside the monitoring image, so that the present invention can rapidly and effectively identify the real object contour by excluding interference of the illumination change without heavy computation, and the image identification method can have advantages of decreasing computation data, economizing hardware cost and shortening a computation period because the present invention has no heavy computation. The image identification method of the present invention can be executed by a device with limited computational resource, such as the common camera, for immediately completing functions of detecting and filter the illumination change. 
     These and other objectives of the present invention will no doubt become obvious to those of ordinary skill in the art after reading the following detailed description of the preferred embodiment that is illustrated in the various figures and drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of an image identification device according to an embodiment of the present invention. 
         FIG. 2  is a flow chart of an image identification method according to the embodiment of the present invention. 
         FIG. 3  is a diagram of a monitoring image without illumination change according to the embodiment of the present invention. 
         FIG. 4  is a diagram of the monitoring images respectively captured before and after the illumination change according to the embodiment of the present invention. 
         FIG. 5  is a diagram of statistic information related to the monitoring images according to the embodiment of the present invention. 
         FIG. 6  is a diagram of statistic information related to the monitoring image according to another embodiment of the present invention. 
         FIG. 7  is a diagram of statistic information related to the monitoring image according to another embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Please refer to  FIG. 1  to  FIG. 3 .  FIG. 1  is a functional block diagram of an image identification device  10  according to an embodiment of the present invention.  FIG. 2  is a flow chart of an image identification method according to the embodiment of the present invention.  FIG. 3  is a diagram of a monitoring image without illumination change according to the embodiment of the present invention. The image identification method illustrated in  FIG. 2  is suitable for the image identification device  10  shown in  FIG. 1 . The image identification device  10  can include an image receiver  12  and an operation processor  14  electrically connected to each other. The image receiver  12  can be used to receive a plurality of monitoring images. The operation processor  14  can execute the image identification method according to the plurality of monitoring images, for excluding some area having the illumination change but no foreground variation inside the monitoring images to increase image identification accuracy. 
     If a vehicle appears in a monitoring region of the image identification device  10 , vehicle light may result in noise of the foreground when executing the image identification, and the image identification method of the present invention can be applied to filter the noise resulted from the vehicle light within the monitoring image for acquiring an accurate identification result. As shown in  FIG. 3 , the monitoring image I 1  not processed by the image identification has the motorcycle, and a region illuminated by the vehicle light is marked by obliquely crossed lines; in the monitoring image I 2  processed by the image identification, a region related to a moving object, such as the motorcycle, is marked by grids, therefore a contour of the motorcycle can be marked through the grids and some region irrelevant to the motorcycle contour can be also marked due to reflection or scattering of the vehicle light. The image identification method of the present invention can identify and filter the region (which is marked by oblique grids) irrelevant to the motorcycle contour but reserve other regions (which is marked by hollow grids) relevant to the motorcycle contour. 
     Please refer to  FIG. 4  and  FIG. 5 .  FIG. 4  is a diagram of the monitoring images respectively captured before and after the illumination change according to the embodiment of the present invention.  FIG. 5  is a diagram of statistic information related to the monitoring images according to the embodiment of the present invention. With respect to the image identification method, step S 200  is executed that the image receiver  12  can receive a first monitoring image F 1  and a second monitoring image F 2  respectively captured before and after the illumination change. The vehicle inside the first monitoring image F 1  does not turn on the light, and the vehicle inside the second monitoring image F 2  turns on the light. A region related to the vehicle light can be marked by oblique lines. Then, step S 202  is executed that the operation processor  14  can divide the first monitoring image F 1  and the second monitoring image F 2  respectively into a plurality of areas, such as a first transforming image F 1 ′ and a second transforming image F 2 ′ shown in  FIG. 5 . Each area may contain one pixel, and color of the area is performed by a value of the foresaid pixel. Each area may further contain a matrix having a plurality of pixels, and color of the area is performed by an average value of the plurality of pixels. The first transforming image F 1 ′ and the second transforming image F 2 ′ can correspond to the first monitoring image F 1  and the second monitoring image F 2 , respectively. 
     Then, step S 204  is executed that the operation processor  14  can respectively compute pixel difference between each area of the second monitoring image F 2  (or the related second transforming image F 2 ′) and a corresponding area of the first monitoring image F 1  (or the related first transforming image F 1 ′), such as the pixel difference between the area A 1  and the area A 1 ′, and the pixel difference between the area A 2  and the area A 2 ′. The pixel difference can equal a pixel value of one area minus a pixel value of other area, or can be an absolute value of a result equals the pixel value of one area minus the pixel value of other area. The preferred embodiment may use the absolute value of the pixel difference. Then, steps S 206 , S 208  and S 210  is executed that the operation processor  14  can classify (or cluster) the pixel difference corresponding to the plurality of areas into one or more groups, and set a threshold and then determine whether distributed concentration of each group conforms to the threshold. As the distributed concentration of one group does not conform to the threshold, step S 212  can be executed to execute the image identification via an area related to the said group, such as the hollow grids shown in  FIG. 3 . As the distributed concentration of one group conforms to the threshold, step S 214  can be executed to define an area related to the said group belonging to an interfered area (which should be filtered), such as the oblique grids shown in  FIG. 3 , and the image identification is executed via some of the plurality of areas except the interfered area. 
     In step S 206 , if the pixel difference is classified into one group, the image identification method can compare the foresaid group with the threshold to find out the interfered area. If the pixel difference is classified into several groups, the image identification method can set a selective condition; when each distributed concentration of several groups conforms to the selective condition, the areas related to the several groups belongs to the interfered area, so that the image identification can be executed via some of the plurality of areas except the interfered area. When the distributed concentration of one or several groups does not conform to the selective condition, the group not conforming to the condition is represented as the non-interfered area. The image identification method can optionally establish the statistic information according to pixel difference about the plurality of areas, and the statistic information is a histogram map H 1  of the pixel difference to an amount of pixels. When the pixel difference are gathered in somewhere of the histogram map, the area related to the group belongs to a region inside the monitoring image affected by the illumination change and prepared to be filtered as long as the distributed concentration conforms to the threshold. 
     The image identification method can use a k-means algorithm to classify the pixel difference corresponding to the plurality of areas within the statistic information, and an actual application is not limited to the above-mentioned embodiment. The foresaid threshold and the selective condition can be defined as variance of statistics. The variance can indicate an average distance between each datum and an average number, and be an index for measuring a degree of data distribution and determining whether the distributed concentration of each group conforms to a filtering condition. The present invention further may use other statistical method to decide the distributed concentration of each group, which depends on design demand. 
     As shown in  FIG. 5 , pixel variation between areas of the first transforming image F 1 ′ and the second transforming image F 2 ′ may be nearly the same or similar values, which means the monitoring image is affected by the illumination change. Please refer to  FIG. 6 .  FIG. 6  is a diagram of statistic information related to the monitoring image according to another embodiment of the present invention. If the monitoring image does not have the illumination change and shows real object motion inside the monitoring region, the pixel variation between the areas of the transforming images F 3  and F 4  can be randomly generated. When the pixel difference between each area of the transforming image F 3  and a corresponding area of the transforming image F 4  is classified, the image identification method can determine that the distributed concentration of the group does not conform to the threshold, which means the pixel difference of a histogram map H 2  based on the statistic information is dispersed; in the meantime, the image identification method can determine the pixel variation between the transforming images F 3  and F 4  belongs to the real foreground variation instead of the illumination change. 
     Please refer to  FIG. 7 .  FIG. 7  is a diagram of statistic information related to the monitoring image according to another embodiment of the present invention. Two monitoring images captured before and after the illumination change can respectively be transforming images F 5  and F 6 . The image identification method can acquire the statistic information shown in a histogram map H 3  after classifying the pixel difference between areas of the transforming images F 5  and F 6 . As shown in  FIG. 7 , the transforming images F 5  and F 6  have local illumination change, which means a lower part of the monitoring image is dark and an upper part of the monitoring image is unvaried, and the pixel difference between the transforming images F 5  and F 6  within the histogram map H 3  can be classified into two groups. The image identification method can decide whether the pixel difference of each group is greater than a critical value T. For example, the pixel difference of the left-side group is close to zero, and related areas can be indicated as a part of the monitoring image without the illumination change; the pixel difference of the right-side group is large and over the critical value, so that other related areas can be indicated as other part of the monitoring image having the illumination change, and belong to the interfered area prepared to be filtered. 
     In conclusion, the image identification method and the image identification device of the present invention can compute the pixel difference between areas from different monitoring images captured before and after the illumination change, and classify the pixel difference of all areas to determine whether one of the groups has the greater distributed concentration. The area related to the group having the greater distributed concentration (which means conforms to a specific condition) can be represented as the interfered area affected by the illumination change and having equivalent pixel variation inside the monitoring image, so that the present invention can rapidly and effectively identify the real object contour by excluding interference of the illumination change without heavy computation, and the image identification method can have advantages of decreasing computation data, economizing hardware cost and shortening a computation period because the present invention has no heavy computation. The image identification method of the present invention can be executed by a device with limited computational resource, such as the common camera, for immediately completing functions of detecting and filter the illumination change. 
     Those skilled in the art will readily observe that numerous modifications and alterations of the device and method may be made while retaining the teachings of the invention. Accordingly, the above disclosure should be construed as limited only by the metes and bounds of the appended claims.