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The electronic image stabilization algorithm based on gray projection is a fast method to stabilize the image. The disadvantage of finding this method in the study is that the accuracy is not high enough, especially in the case of complex images and local motion. A new method of combining gray level planes and grayscale projection algorithms is proposed. This method calculates the results accurately by calculating the mean of the offset of multiple bit planes. The experimental results show that the method can reduce the computational error effectively. Introduction Urban traffic real-time monitoring system is an important part of intelligent transportation. At the intersection and the important sections, more and more monitoring equipment is installed to monitor the traffic. Traffic monitoring video is an important source of data for traffic analysis, congestion prediction and evaluation, etc. Therefore, image processing is one of the hotspots of intelligent transportation. Electronic image stabilization techniques include a variety of methods, such as gray projection method, block matching method, representative point method, and phase correlation method, in which the gray scale projection method is the fastest and most widely used. At present, there are a lot of research on gray projection methods, including algorithms and application platforms [1, 2]. Wang (2012) studies electronic image stabilization algorithm based on gray projection block matching. The proposed method summarizes the advantages of grayscale projection algorithm with fast computing speed and block matching algorithm with high accuracy of matching, and has a good image stabilization accuracy and image stabilization speed by experimental verification [3]. Zhao (2013) studies the relationship between clustering and grayscale projection techniques, and an electronic image stabilization method combining clustering and gray projection is proposed. The motion estimation of the rotation angle is estimated using wavelet transform and clustering methods and the motion estimation of the translation is calculated using the gray scale projection algorithm [4]. Gu (2013) describes problems with the effect of poor image stabilization and the possibility of producing erroneous image stabilization when the gray scale projection algorithm is applied to the presence of local motion targets. On the basis of analyzing the basic theory of gray projection, an improved gray projection algorithm is proposed, and this method can solve the situation of the existence of moving objects [5]. CZ You (2015) studies the time-consuming problem when the gray-scale projection algorithm was in a stable image. In the case where the image stabilization effect is not affected, in the case that the image stabilization effect is not affected, this method distinguishes the image and uses the improved algorithm to solve the gray scale projection vector, and uses the mean filter to get the global motion vector to realize the motion compensation [6]. Gong (2017) studies the problem of insufficient accuracy of grayscale projection. He finds that the gray scale projection algorithm accuracy would drop when dealing with gray value of a single, poor contrast image sequence. And an electronic image stabilization algorithm based on block gray scale projection is proposed [7]. Fan (2017) studies the mine car camera system. In order to quickly and accurately stabilize the jitter image obtained from the system, an electronic image stabilization algorithm based on down-sampling gray-scale projection is proposed. This method is superior to traditional gray-scale projection algorithms in terms of accuracy and computation time [8]. 143 In the above view, it is shown that the research direction of this algorithm is to improve the image stabilization accuracy. Therefore, this paper focuses on the development of this method and proposes an improved algorithm for gray image projection based on bit – plane. Electronic Image Stabilization and Gray Projection and Bit-plane Electronic Image Stabilization Based on Gray Projection Algorithm EIS [9] is a way that we can use the method of digital image processing to make images stable. It includes digital image acquisition through electronic equipment DV, DC, camera, digital image processing and digital image output and so on. Figure 1. Flow chart of electronic image stabilization. GPA includes four steps as follows: Step 1: It is necessary to change the image into grayscale image. Step 2: It will get the row and the column projecting value through gray projection operation. Computing formula is as follows.    n j j i pic i row 1 ) , ( ) ( ;    m i j i pic j column 1 ) , ( ) ( (1) ) (i row is the row projecting value on i line. ) ( j column is the column projecting value on j series. ) , ( j i pic is the gray value on the point of ) , ( j i . Step 3: The image dithering offset will be got through correlation operation. This formula is as follows.   2 1 ) ( ) 1 ( ) (        n j j M column w j column w C ; 1 2 1    M w (2) ) (w C is the correlation operation value. M is the maximum extent of detection jitter in image. min W is the w when ) (w C is minimum. Now, the offset can be computed through the following formula. The column offset is computed, so is the row offset. min 1 W M c motion    (3) Step 4: Move the dithering image r motion and c motion volumes in the opposite direction, and then we can acquire the stable image.

An Improved Electronic Image Stabilization Algorithm Based on Gray Projection