Abstract— Slow change in light intensity will record on the image. The selection timing on sequence image samples and the method that how to reconstruct the background have very important impacts on the accuracy of dynamic background reconstruction. As more complexity of urban road junction traffic information, the existing background reconstruction algorithms have some shortcomings. We present a dynamic background reconstruction algorithm on enhanced light adaptability. It effectively reduces background reconstruction errors caused by the slow change of light intensity. Experimental results show the algorithm is simple, real strong, and reliable, have strong practical value.

I INTRODUCTION ITH the dramatic increase in city vehicles, traffic congestion is becoming common and worse. Especially at crossroads, jam is more serious.

Currently the traditional lamp signal control mainly adopts fixed mode or half fixed mode, it doesn’t control intelligently according to the real traffic flow. If the junction signal can be controlled by real-time traffic flow information, not only can save manpower resources, but also can improve road capacity, relieve urban road congestion.

Video-based detection methods of moving objects are various, such as optical flow[1], o-frame difference method [2] and background subtraction [3,5], etc. Actual traffic information on the road junction is very complex, such as speed changes rapidly, the directions of vehicles are various, traffic jam and so on. Because of the background subtraction algorithm is simple and computing fast, it can be used in the junction traffic flow detection. However, how to reconstruct the road background image accurately is the primes and key to get the accurate real-time traffic flow information.

Currently the algorithms of background construction can be divided into static and dynamic types. Static methods are poor in adaptability on environment, and its accuracy is low. They are not suitable for real-time junction background reconstruction. The dynamic background modeling methods such as the average estimation [7], based on the frequency of the background estimation method and speed of field theory based on the background estimation method [6,8] and so on. Due to the large computations speed field theory are not very fast on background reconstruction. And the results will be not accurate when traffic congestion begin. The average estimate and the frequency estimation algorithm is simple and good in real time. But the average estimate in traffic flow is large, the error is also great, not suitable for crossing the background

Shuan Liu is with Department of Computer Science, Huanghuai University, Zhumadian, China ; e-mail: sendtols2004@sina.com).

Ningbo Hao is International College, Huanghuai University, Zhumadian, China.

modeling. The estimated frequency of background change to light adaptation is also poor.

So, this paper, a dynamic background reconstruction algorithm is presented. Not only does it effectively reduce background reconstruction errors, but also it can adapt the slow changing of light.

II SIMPLE STATISTICAL BACKGROUND ESTIMATE ALGORITHM In practice, traffic flow is large, there will be any vehicles

blocking the road, the system initialization phase is difficult to obtain accurate background information, this affects the validity of the method based on background reconstruction.

Dan Gutchess presents an average estimate background algorithm[2]. If the definition ),( jiB is the point ),( ji estimate value of gray background, N is the number of image frames, ),( jif t is the t frame image point ),( ji gray value, that is,

∑=

=N

tt jif

NjiB

1),(1),( (1)

But, this approach even without plugging in the road, since the estimated value of foreground and background pixels is a sequence of images the average pixel values, so the error undoubtedly exists, which is known as "shadow" noise shown in Fig.4.

A. Simple Frequency Estimate Theory In the actual traffic, it is not a very common phenomenon

that the traffic blocks for very long time. So if the control of the signal light can be more intelligent, that can be further effectively reduced the possibility of such a serious traffic jam. Therefore, know by experience, in the recent period of time, the road information is always visible, because when the signal light is green, the road junction of the traffic is always moving, so that the road can always show out at a moment. So form multi-frame images the same pixel value occurs most frequently, it should be the same point value of the background reconstruction.

This method based on the statistical frequency estimation can accurately and timely access to the background image information. If the definition ),( jiB is the point ),( ji value of the gray background reconstruction, N is the number of image frames, ),( jif t is the value of the point ),( ji in

the t frame image, the array ][kC is used to store the frequency of pixel value occures, that is,

[ ] [ ] 1+= kCkC ,

.255,...,1,0;,...,1;),( === kNtkjif t

An Enhanced Light Adaptability Algorithm on Dynamic Background Reconstruction

Shuan Liu and Ningbo Hao

W

International Conference on Intelligent Control and Information ProcessingAugust 13-15, 2010 - Dalian, China

978-1-4244-7050-1/10/$26.00 c©2010 IEEE 252

b

b

a c

a b c

( ) 255,...,1,0kmax][, == ，， iskwhereCkjiB (2)

The pixel value of the background reconstruction is the highest frequency value which appear in the sequence of images, that is, ][kC is the largest gray value k.

The corresponding relationship between the gray value and the frequency values plotted in Fig. 1.

Fig.1. The relationship between the gray value and the frequency values

In Fig.1, the horizontal axis represent gray value, ranging from 0 to 255, the vertical axis value for the statistical frequency up to N (picture frames). Background image is the N frame image appears most frequently, in Figure1, the point value is to choose the frequency corresponding to the maximum gray value.

Obviously, this choice is flawed. First, the highest frequency of gray value is not necessarily the road background information, it may be noise, and it an also be the vehicle through the same color. Secondly, if ][kC there are two or more of the same highest value, it is very difficult to select the right value. Third, because of discrete sampling which selected in some time interval, the same pixel values can be reflected very sensitive by light change, if using (2), it will be hard to apply the best judge. Therefore, this paper presents a dynamic background reconstruction algorithm on enhanced light adaptability.

B. Improved Algorithm on Enhanced Light Adaptability Knowing by experience, the gray value fluctuates in a

small range with the light slow changing. Suppose a gray value of a pixel, then the same pixel value of following sequence of images should be as follows

TjiBjif t ±= ),(),( （3） T is an integer number, it reflects change range.

Fig.2. Enhanced light adaptability

If we take values which changing in the range of T as the same level, it would effectively reduce the errors by light slowly changing. Shown as Fig.2, we select the max sum in a T range. Then we take the average value as the same pixel gray value of background reconstruction.

Array ][kC is defined to store the statistical frequency of pixel values, k = 0,1, ..., 255, array ][tg is defined to store

the sum of T range statistical frequency ，

Tt −= 255,...,1,0 ，T is a integer number, 0there is the following formula,

∑+

=

−==Tt

tkTtkctg ;255,...,1,0],[][ （4）

The pixel ),( jiB value of background selects the max from array,

Tt

istgwheremT

jiBTt

tm

−=

= ∑+

=

255,...,1,0

max,)(),(1),( （5）

C. Selection of T As the real-time background reconstruction is sensitive to

light changing, in the formula (5) T can not be used in a fixed value, it should also change with the change of samples. If we use N frames image to construct the background image, select m pixels which will not be covered from the images, know by experience, as the light changes, the maximum pixel value changes are between the first frame and the last frame. Define array ][iD to store the difference value of first frame and last frame, there are:

.1,...,1,0|,),(),(|][ 10 −=−= − miyxfyxfiD iiNii （6）

Then sorts the ][iD data, select the mid number as the change range T. With this improved method to construct the background, reducing the affects of light change.

D. Image Denoising[9] As the images collected are often affected by light and

other external environmental impacts, in order to reduce the noise on image quality, it is needed to reduce noise.

In order to meet the need of real time background reconstruction, using noise smoothing algorithm in adjacent regions can get good results.

Defined the value ),( yxf as the pixel (x,y) value of NN × image, after noise smoothing the gray value of the

point are,

∑∈

=Snm

nmfM

yxg),(

),(1),( (7)

Formula (7), ,1...,2,1,0, −= Nyx S is the set of coordinates of the pixel, but does not include (x, y) point, M is the total number of collection points within the coordinates

Although this method is effective on reducing noise, but with the increasing of adjacent field, the image blurring become even more serious. To overcome this, the threshold method is used to reduce the fuzzy effect. The basic method is as follows:

⎪⎩

⎪⎨

⎧ >−

=

∑∑∈∈

otheryxf

T|f(m,n)M

|f(x,y)f(m,n),M

yxg

S(m,n)S(m,n)

),,(

11),(

(8)

gray value

gray value

statistical frequency

T

statistical frequency

253

In (8), T is a non-negative threshold value, so that when the mean pixel gray area does not exceed the threshold T, to retain its gray value remains unchanged, otherwise, then instead of using the mean gray areas. This method can significantly reduce the degree of ambiguity, signal to noise ratio can be 2

1

)( nm × doubled.

III EXPERIMENTAL PROCEDURES AND RESULTS ANALYSIS

A Experimental Procedures 1）Selecting background images. When the signal lights are

green, to capture image, because at the time traffic began to move, so the information of the road background can be seen, easy to construct the image, we set collection interval 5 seconds.

2）Discrete samples collected with 30 frames to complete the background modeling. Samples require to collect in 10 minutes, when a construction background junctions image of the sample interval less than 10 minutes, so in the improved statistical frequency algorithm, the T can be a small data, which is better to improve the accuracy of background reconstruction.

3）Get the current background image. For the last step to complete the background image, according to the (8), using the improved adjacent field threshold denoising, can get the current background image.

E. Results analysis This algorithm is realized by using vc ++6.0, BMP image,

the original size is1728×1152. Below are some of the experimental images from many

experiment times: Fig 3 is an experimental image which is one of 20 frame

images. Fig 4 is a local enlarging part of a background which is

reconstruct by the Gutchess Dan statistical average method. In experiments we find that “shadow” that the errors of background will be very serious when traffic flow is heavy.

Fig 5 is a local enlarging part of a background which is reconstruct by the statistical frequency methods. We can clearly see the image of the error, but the effect is still better than average estimate method.

Fig 6 is a local enlarging part of a background which is reconstruct by the improved statistical frequency methods. And it can clearly see that accuracy has been greatly improved .

Fig 7,8 are the local enlarging part of background differentiating image by using statistical frequency algorithm and improved statistical frequency algorithm. Through the contrast can clearly see that the enhanced light adaptability algorithm is more closer to the real background, the errors of the background subtraction was decreased obviously.

Fig.3 Road junction traffic image

Fig.4. Local part of average algorithm

Fig.5. Local part of frequency algorithm

Fig. 6. Local part of enhanced light adaptability algorithm

Fig.7. Local part of background differentiating by frequency algorithm

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Fig 8. Local part of background differentiating by enhanced light adaptability

algorithm

CONCLUSION In this paper the enhanced light adaptability algorithm does

not only keep the good real-time performance, but also enhance the light adaptability. In experiments the accuracy is improved clearly. The enhanced light adaptability algorithm on dynamic background reconstruction can be used in intelligent signal control based on video.

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