Vision-Based Vehicle Speed Measurement System

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Journal of Computer Applications (JCA)

ISSN: 0974-1925, Volume V, Issue 3, 2012

82

Abstract - Vision-based vehicle speed measurement

(VSM) is one of the most convenient methods available in

intelligent transportation systems. A new algorithm for

estimating individual vehicle speed based on two

consecutive images captured from a traffic safety camera

system. Its principles are first, both images are

transformed from the image plane to the 3D world

coordinates based on the calibrated camera parameters.

Second, the difference of the two transformed images is

calculated, resulting in the background being eliminated

and vehicles in the two images are mapped onto one

image. Finally, a block feature of the vehicle closest to the

ground is matched to estimate vehicle travel distance and

speed.

Index Terms images, vehicle, vehicle speed measurement.

I. INTRODUCTIONIntelligent transportation systems are becoming more

important due to their advantages of saving lives, money, and

time. Acquiring traffic information, such as lane width traffic

volume (the number of travelling vehicles per time period

through a position in a lane), traffic density (the number of

total vehicles in a given area at a given time) and vehicle

speed, these are the key part of intelligent transportation

systems, and such information is used to manage and control

traffic. It focuses on vehicle speed since reducing speed can

help to reduce accidents.

Intelligent transportation systems is a new approach to

estimate vehicle speed using video sequence data recorded

form roadway surveillance cameras under occlusion. Rather

than detecting or tracking individual vehicles in each image

frame as is conventionally done, It transform the sequence of

frames into a graphical representation of vehicle trajectories

using time stacks. Where a time stack is constructed from a

video sequence out of temporally successive slices of the

same 1D region in the frame. Most vision based trafficmonitoring systems (VBTMS) attempt to detect and track

discrete vehicles. But such segmentation and tracking is

hampered by occlusion, changes in lightning conditions and

shadows. Many Solutions have been developed, Segmenting

discrete vehicles for detection and tracking in video is

difficult, measuring the speed of vehicle features is much

simpler and so this work seeks to bypass the need for

segmenting each vehicle when measuring traffic speed over

Manuscript received 8/Sept/2012.

Manuscript selected 11/Sept/2012.

Prakash.V,Sri Ramakrishna Institute of Technology , Coimbatore, India

E-mail: [email protected]

Devi.P, Assistant Professor, Dept of ECE, Sri Ramakrishna Institute of

Technology , Coimbatore, India, E-mail: [email protected]

time and space. Proposed system consists of 5 steps to

estimate mean speeds:

(1) Camera calibration from image to world co ordinatetransformation to generate velocity fields,

(2) Video stabilization to decrease unwanted cameramotion in the video sequence,

(3) Pre-processing using edge removal apply imageenhancement for background estimation and shadow

removal,

(4) Background estimation and shadow removal,(5) Cubic spline interpolation for trajectory

approximation.

The overall intention is to implement a vision-based speedsensor considering occlusion caused by overlapping vehicles.

It is handling the occluders rather than the occluded vehicles.

For almost two decades these CCTV systems were strictly

used to extend the view available to human traffic managers.

The objective behind these video-based traffic surveillance

systems is to extract important traffic parameters for traffic

analysis and management such as vehicle speed, vehicle path,

flow rate and density. Vision-based vehicle detection is

intended to be a lower cost alternative to many other detector

technologies, e.g., loop detectors. Loop detectors are

essentially metal detectors embedded in the pavement, and

have been the most common detector to record trafficparameters. Depending on deployment these detectors can

collect information such as vehicle length, speed and flow

rate. However, loop detectors only detect vehicles directly

overhead. In contrast, vision-based systems collect

information over an extended space compared to the

conventional vehicle detection systems.

II. BRIEF REVIEW OF VISIONBASED VEHICLE SPEEDMEASUREMENT SYSTEMS

The block diagram of the proposed algorithm is depicted in

Fig. 1. To begin with, the two consecutive images taken

through a perspective view are transformed from their 2Dimage plane to 3D world coordinates using camera

parameters calculated from lane-marking calibration. As there

is no height information immediately available from the two

images, only the X-Y plane in 3D is considered for all

subsequent estimation. From the two reconstructed images, an

image differencing is applied so that the two vehicles are

overlaid on one resultant image. Although this can effectively

remove the background features as the time difference

between the two images is small, it also resulted in a large

amount of noise and holes along the boundaries of the

vehicles. These are removed by a carefully selected threshold

and morphological operations to obtain a combined vehicle

mask. After that, a block matching algorithm is applied, which

includes specifying the block and matching it within a search

window. As the 3DX-Yplane is given in physical dimension

(meters in this case), he distance between the two matched

Vision-Based Vehicle Speed

Measurement SystemPrakash.V a,*, Devi.P b,1


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Journal of Computer Applications (JCA)

ISSN: 0974-1925, Volume V, Issue 3, 2012

84

noted that this is only true for points on the road plane. Other

points that are not on the road plane do not represent the same

as height information is not available. Therefore, the problem

in matching is not finding the most correlated blocks between

the two vehicle images as, but to make sure that the feature

points in the block are on the road surface as well. The most

reliable points in these images for the purpose are the points

where the wheels touch the ground. Unfortunately, these

points are extremely difficult to be determined. For the reasonthe subsequent estimation is based on the shadow of the back

bumper that casts on the ground. Although this feature is

relatively easy to find, this is also represents a source of error

due to road colour change, illumination change and shadow.

Background Componsation and Vehicle Detection

Separating the vehicle from the road (background) is a major

issue in detecting a vehicle from a captured image. The

background image is an image that contains no moving

objects. As the roads scene changes every second, it is

impossible to get a consistent background image from a single

image and create the empty background image with a series of

images containing the moving objects to create an empty

background image from a few captured images.

This algorithm uses the Gaussian mean and the standard

deviation between pixels from each image to ignore the

absurd value pixels that represent the moving object.

Background subtraction computes the inter frame difference

(IFD) between the background image and the incoming image

from the camera. The result of the IFD process, i.e., the

foreground image, involves a black background with floating

vehicles. Image difference is a widely used method for the

detection of motion changes. The interframe difference (IFD)

is calculated by performing a pixel-by-pixel subtraction

between two images.. In particular, after creating thebackground, displacements can be seen between the incoming

frames and the generated background.

Moving Region Extraction

If a background reference image is available, it can easily

extract the vehicle masks of each image and estimate the

vehicle position directly.

On the premise that no background reference image is

available, it regard one of the two images as a background

reference, then perform conventional vehicle mask extraction

on the other image and finally get an overlaid vehicle mask.

Morphological ProcessA simple vehicle mask extraction method was adopted, which

includes three steps: temporal differencing, thresholding and

morphological operations. First, It overlay one image onto the

other by taking an absolute difference between the two

images. This results in the image as depicted in Fig.5(a). For

colour images and obtain the absolute difference in each

colour channel first and then average them to a gray level

image. The image depicted in Fig. 5(a) is then threshold to

produce a binary image as depicted in Fig.5(b). The threshold,

T, is a predefined value to accommodate small pixel intensity

variation. After that, morphological opening is used to clean

up all the isolated points and morphological closing is used tojoin the disconnected vehicle components together, resulting

in the extracted vehicle mask as shown in Figures.

(a) (b)Figure 5. (a) Binary image after thresholding.

(b) Vehicle mask after morphological operations.

In general, the case as depicted in Fig. represents a typical

scenario. By computing the connected components in the

binary image, it has two blobs which represent the masks of

two individual vehicles. From the distance of the bottom edge

of the two blobs, displacement of the vehicle in two

consecutive frames can be roughly estimated, which could be

refined by block matching. However, for vehicles that are

travelling at low speed, or have long length or the two

consecutive images are taken at a shorter time.

Block Matching

In this Section, the block matching algorithm (BMA)

estimates motion between two images or two video frames

using "blocks" of pixels. First, a source block is defined in

terms of a horizontal base line, a fixed height and a variable

width according to the size of the mask. The blob shown in

Fig.6 is part of the lower blob from the masking image, in

which a horizontal base line is drawn across the lowest part of

the blob. From the base line, an upper boundary of k pixelabove the line and a lower boundary also of k pixels below the

line are defined. As the upper boundary line intersects the

vehicle mask, the left and right boundaries are defined by the

width of the mask intersected by the upper boundary line. This

defines a source block of 2kw in size.

Figure 6. Definition of Source Block.

With these boundaries, it has a block defined in the first

reconstructed image as the source block. The search is

performed in the second reconstructed image. The mean

absolute difference (MAD) measurement between the source

block and candidate blocks is employed as a criterion in

proposed matching algorithm and roughly estimated

displacement of the vehicle. The underlying supposition

behind motion estimation is that the patterns corresponding to

objects and background in a frame of video sequence movewithin the frame to form corresponding objects on the

subsequent frame. The idea behind block matching is to

divide the current frame into a matrix of macro blocks that


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Vision-Based Vehicle Speed Measurement System

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are then compared with corresponding block and its adjacent

neighbours in the previous frame to create a vector that

stipulates the movement of a macro block from one location

to another in the previous frame. This movement calculated

for all the macro blocks comprising a frame, constitutes the

motion estimated in the current frame. The search area for a

good macro block match is constrained up to p pixels on all

fours sides of the corresponding macro block in previous

frame.

Figure 7. Travel Distance Estimation

Velocity Measurement

Once a block is matched, travelled distance in pixels, PD, of

the vehicle between the two images is represented by the

distance between the source block and the matched block.

Vehicle speed is the distance travelled normalized to physical

distance in m, and divided by the time taken to traverse that

distance, which is given by,

Where, PD - Travelled distance in pixels,R - Resolution of reconstructed images,

t - Time interval between the two images,

v - Estimated speed

III. EXPERIMENTAL RESULTS

Figure 8. Speed of the vehicles

In this experiment, we evaluate the accuracy of the proposed

method in estimating individual vehicle speed from real

traffic images at day-time as well as night-time, by comparing

the estimated value with the speed measured by a Doppler

speed radar system. All the test images are taken by a

surveillance camera at 0.5 seconds apart, with the size of

12801024 and 24 bit color depth mode. We first calculate

the reconstructed images as described in Section 2.2 with

resolution of 10 pixels/meter, and then threshold the 256 gray

level frame difference using a threshold of 30 to generate the

vehicle mask. k, sxand syin block matching period were set to

10 pixels, 20 pixels and 10 pixels respectively. For the 31 sets

of day-time images, the results estimated by the proposed

method and reference speed.

IV. CONCLUSION AND FUTURE DIRECTIONIn conclusion, a novel method for estimating individual

vehicle speed using two consecutive images from a traffic

surveillance camera has been proposed. Compared with speed

from radar, the averaged estimation errors for day-time cases

is 3.27%, while for night-time cases is 8.51%. Future work

will be focused on the block matching accuracy and the

robustness of the algorithm against large changes in ambient

illumination conditions.

REFERENCES

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BIOGRAPHY

V. Prakash received the BE degree in Electronics and

Communication Engineering from Sri RamakrishnaInstitute of Technology, Anna University Coimbatore

in 2012. He is an active member of IETE. He had

presented national conferences and international

conferences in various fields He had done the project

in the area of Digital Image Processing. His area of

interest is Vehicular technology, Digital Electronics, Digital Image

Processing.

P. Devi received the BE degree in Electronics and

Communication Engineering from VLB Janakiammal

College of Engineering and Technology, Anna University

Chennai in 2009 and the ME degree in Communication

Systems from Sri Krishna College of Engineering and

Technology, Anna University Coimbatore in 2011.

Currently working in Sri Ramakrishna Institute of

Technology as an Assistant Professor in ECE Department.She has one year teaching experience. She has presented the papers in the

national conference. Under her guidance, the final year students are doing

their project in various fields. Her field of interest is Digital Image

Processing.

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Vision-Based Vehicle Speed Measurement System