This paper reports an improved method for processing the image of a vehicle's license plate when shooting with a smartphone camera. The method for processing the image of a vehicle's license plate includes the following stages:
– enter the source data;
– split the video streaming into frames;
– preliminary process the image of a vehicle's license plate;
– find the area of a vehicle's license plate;
– refine character recognition using the signature of a vehicle's license plate;
– refine character recognition using the combined results from frames in the streaming video;
– obtain the result of processing.
Experimental studies were conducted on the processing of images of a vehicle's license plate. During the experimental studies, the license plate of a military vehicle (Ukraine) was considered. The original image was the color image of a vehicle. The results of experimental studies are given. A comparison of the quality of character recognition in a license plate has been carried out. It was established that the improved method that uses the combined results from streaming video frames works out efficiently at the end of the sequence. The improved method that employs the combined results from streaming video frames operates with numerical probability vectors.
The assessment of errors of the first and second kind in processing the image of a license plate was carried out. The total accuracy of finding the area of a license plate by known method is 61 % while the improved method's result is 76 %. It has been established that the minimization of errors of the first kind is more important than reducing errors of the second kind. If a license plate is incorrectly identified, these results would certainly be discarded at the character recognition stage.
The configuration of the video monitoring system, which is widely used in the protection systems of objects and positions of units, is given. Examples of the collision line video monitoring system are given, namely: the video monitoring system of the special monitoring mission of the Organization for Security and Cooperation in Europe and the battalion video surveillance complex. An analysis of known methods of image segmentation in video monitoring systems has been carried out. The analysis showed that these methods do not meet the requirements for the quality of image segmentation in the video monitoring system. The image segmentation method in the remote video monitoring system has been improved, in which, unlike the known ones: the source image is divided into RGB brightness channels; determination of the Euclidean distance between pixels; division of the entire set of image pixels into subsets; recalculation of “centers” of each subset; reassignment of new “centers” of each subset; minimization of total intra-cluster variance. Experimental studies of image segmentation from the remote video monitoring system were conducted. The image from the video monitoring system on the contact line during hostilities during the russian-ukrainian war is used as the source image. The segmentation results of this source image are given. It was established that there is a visual possibility of identifying objects of interest on segmented images. At the same time, it is possible to visually determine the elements of the protective cape only at k=4, while at k=2 and k=3 this possibility is complicated. The directions of further research are the determination of the optimal value of the value k when using the k-means algorithm and the evaluation of the quality of image segmentation in the remote video monitoring system.
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