IAI' Clli KIIOA IIQC * C0N(1 NClIf CAC TRtftNC BAi H<JC KiTHUiT * S6 91 -2012
THE DESIGN OF A TRAFFIC LEVEL OF SERVICE ESTIMATION EMBEDDED SVSTEM
THET KE HE THONG NHUNG V(5l C H C C N A N G HOC LI/ONG MLTC GIAO THONG Pltam Ngoc Nam
Ha Noi University of Science and Technology Received Fcbruaiy 28, 2012; accepted October 01, 2012
ABSTRACT
Traffic management is a big problem for big cities in Asia in general and in Vietnam in particular.
Information about tlie traffic level of service is crucial for regulating and managing the traftlc. In this paper we present the design of an embedded system which can be used to automatically estimate the traffic level of sen/ice of urban roads. The Information of traffic level of service can then be used by ttie road users or traffic manager to improve the traffic situation. We propose the use of background model and vehicle tracking for LOS estimation and Implement the system on a low cost ARM based platform.
Keyword- Embedded system, traffic level of service. OpenCV T 6 M TAT
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1, INTRODUCTION situation and traffic density on main routes in Traffic jam is a big problem in big cities "^™' I^l' ^'^'•i^ *= camera system, VOV in Asian countries such as India, China and "'^^''"^ channel also has a number of reporters, Vietnam. In Hanoi, the Vietnamese capital, collaborators and a hotline system to collect approximately 1.3 million USD is lost each day ^'"^^"^ information. However, the problem with due to traffic jam [1]. Many measures have *'^ ^^^'''" '^ * " ' ""= '"fonnation about the been talten to taclcle this problem including '"^^"^ ''™^">' '= "'^c" delivered to the drivers traffic engineering solutions and governmem ^"""^ minutes late and that it only covers the policies. One solution which has been very '"^'" ' " ^ ^ '" Hanoi. In this paper, we propose efficient is the introduction of the VOV (Voice "" automatic system which can provide traffic of Viemam) traffic channel. With 330 minutes information from large number of routes at real of live broadcasts during rush hours (6.30am - Hme as shown in Figure I. In our proposed Sam, H a m - 1 2 noon, and 4.30pm-7pm), the system, an embedded device with a traffic information provided by VOV Traffic Channel surveillance camera is installed for each road, has proven to be practical, useful and relevant. ^'"^ device takes the images captured from Ihe Images recorded by 100 cameras in important camera and estimates the traffic level of service traffic spots are transmitted to the VOV Traffic (LOS) based on the traffic density and speed.
Channel Centre and the information is ^'^^ traffic LOS from all the embedded devices communicated quickly to people driving in the ^^^ transmitted to a server over the Internet or a traffic. The information aims to instruct and wireless network such as GPRS or 30. The warn drivers of risks as well as the weather server will then provide traffic informafion to
T ^ CHi KHOA HQC & C 6 N G NGHf CAC TRUftNG D^I HQC KY TIIUAT * Sd 91 - 2012 the users in different ways such as integrating
in an on-line map for supporting route planning.
Fig.l The traffic LOS estimation system The advantages of the proposed system are 1) it is fijlly automated and therefore does not require many human resources as in the VOV system 2) with the use of low cost embedded devices, the traffic situation of a large area can be recorded by installing one device at each road segment 3) the traffic information is almost instant.
The main contributions of our paper are as follows.
- We have developed a new algorithm for estimating the traffic LoS using OpenCV library [3] using both average speed of the traffic and road coverage information.
- We have implemented successfully the system on an low cost embedded ARM kit and developed a web server which receives traffic LOS information and integrates in a Google map application.
The rest of the paper is organized as follows. Section 2 presents the related work.
Section 3 describes the traffic LOS estimation algorithm. The detailed design of a prototype system is presented in Section 4. Conclusions are drawn in Section 5.
2. RELATED WORK
Traffic speed and density estimation has been studied by many researchers. The main approach for speed and density estimation has based on vehicle tracking using background models and feature tracking. [4][5] present background models in different light and
weather condition. In [6] the authors estimate traffic density using texture feature and edge features. Most of these systems are computational intensive and therefore are not suitable for embedded systems. [7] present an estimation approach based on the extraction of three local visual features. The feature set comprises KLT motion vectors and Sobel edges, and is fed into a Gaussian radial-basis- function (GRBF) network to classify the prevailing LOS. The whole approach is designed and implemented to run on an Atom based smart cameras in real-time. To the best of our knowledge this is the only LOS estimation embedded system found in literature. Different from [7], we use background model and vehicle tracking for LOS estimation and implement the system on a low cost ARM based platform.
3. TRAFFIC LOS ESTIMATION 3.1 Traffic Level of Service Model
Level of service (LOS) is a term used to qualitatively describe the operating conditions of a roadway based on factors such as speed, travel time, maneuverability, delay, and safety [8]. In this paper, we propose a traffic LOS model for urban roads in Vietnam with the speed limit of 50 km/h (Table 1). The model is based on the model for US urban roads [9] with some modifications to fit to Vietnam condition.
Table 1. Traffic LOS model LOS
A B
C D
E
Description Free flow (number of vehicles <5) Reasonably free flow (number of vehicles (5,10), mean speed > 30 km/h)
Approaching ustable flow (number of vehicles > 10, mean speed > 1 Okm/h) Unstable flow
(a lot of vehicles, mean speed >
5km/h) Traffic jam
The number of vehicles is counted in the view field of the camera for each observation time window. As can be seen in Table 1, for LOS from A to C, both the number of vehicles and traffic mean speed are used to classify the LOS. For LOS D and E, only traffic speed parameter is used.
TAP cni KHOA HQC & C6NG NGHf: CAC TRITONG DAI HQC K* THUAT*S691-2012 3.2 Traffic LOS Estimation
The traffic LOS estimation algorithm i presented in Figure 2.
Video input
PreproccssingVidco
Delecting Background
Identifying LOS Fig.2 LOS estimation algorithm The funcdon of each step will be explained in detail in the next subsections.
/. Preprocessing video
The purpose of this step is to facilitate the tracking of the captured objects by delecting edges of the objects, A convolution filter is used for this purpose. Figure 3 shows an example of a video image before and after the preprocessing step.
.^ • f
•r.,,
3
. 1 1 1 1 )
Pig.J Video image before a) and after b) preprocessing step
2. Detecting background
The function of this step is to detecting the background of the captured video in order to indentify the moving objects on the foreground The foreground is obtained by subtracting the captured image by the background image.
Gaussian Mixture Model method [10] is used in this step. The background image must be updated regularly so as to adapt to the varying luminance conditions. Figure 4 shows how we can extract the foreground from the background.
r
a)Original image b) Foreground c) Background Fig. 4 An example of background delecting 3. Calculating road coverage
This step is to calculate the road coverage in order to select the next processing step. If the road coverage exceeds a preset threshold the next step is tracking random object and calculating speed which is for LOS D and E;
otherwise the next step is finding object contour which is for the LOD from A to C. We use ID histogram to represent the road coverage.
4. Finding object contours
The function of this step is to find the number of contours, i.e. number of vehicles which will be used to determine the LOS.
5. Tracking objects and calculating speed The function of this step is to track objects and calculate the speed of the objects using Lucas-Kanade method [11]. Figure 5 illustrates how we can calculate the actual speed of a vehicle. Let's consider a point of an object in two consecutive frames with the coordinates (xl, yl) and (x2, y2). The displacement of the point in the two coordinates is calculated by:
Ax = xl - x2 (pixels) and Ay = yl - y2 (pixels)
The actual moving distance of the object on the road measured in meter will be:
TAP CHi KHOA HQC & CONG NGHg CAC TRITONG DAI HQC KY THUAT • S 6 91 - 2012
AD =
Ay
= V A F T ' A F Z (m), where:
AX = A x * Dl/320 (m) and AY ••
*D2/240 (m)
The actual speed of a vehicle is calculated using the following formula:
V = AD * R (m/s) = 3.6 * AD * R (km/h), where R is the frame rate of the video measured in frames per second (fps).
Fig 5 MappingJi-om video to actual size Here we assume the resolution of the video is 320x240. For other resolutions we can easily derive the formulas based on the above formulas. Dl and D2 are measured for each installed position of the camera.
6. Identifying LOS
The function of this step is to identifying the LOS based on the traffic LOS model presented in Table L For LOS A to C the speed is the mean speed of all the objects calculated in the previous step. For LOS D and E, the speed is the speed of the randomly selected object.
3.3 Simulation Results
The traffic LOS estimation algorithm has been implemented using OpenCV functions in Visual Studio environment and tested on a PC.
To collect the data set for the simulation, we installed a camera on a fly-over for pedestrians in Dai Co Viet street and recorded a number of traffic videos with a resolution of 320x240. The videos were recorded during different times of the day and have duration of about 30 minutes.
In order to test the accuracy of the speed estimation method, for each video, we prepared
subjects riding motorbike at different speeds and recorded their actual speeds.
Figure 6 shows the traffic LOS estimation results in the case where the LOS is from A to C while Figure 7 shows the results with LOS from D to E.
Fig, 6 LOS estimation results with LOS from A loC
A: Background B: Foreground C: Object contours D: Histogram R- Actual video F: the LOS estimation resuh
Fig. 7 LOS estimation results with LOS from D toE
As can be seen in Figure 6, the LOS estimation result is B, i.e, reasonably free flow.
In Figure 7, the background contains a lot of image pixels from vehicles. That is because the background is continuously updated with the unmoving pixels and in this case the vehicles are moving very slowly. The histogram also shows that the road coverage exceeds the threshold and therefore the algorithm executed the step tracking random object and calculate speed. In this case, the LOS estimation result is
TAP (Hi KIIOA HQC & C6NG NGH? CAC TRl/OfNG DAI HQC K* THUiJiT • S6 91 - 2012 server via an Internet connection. We have ported embedded Linux operating system in the embedded platform using open source code for better optimization of the porting. The trafific LOS estimation algorithm has then been optimized and ported successfully on the platform. On the server side we have developed a Google map application which allows user to click on a road to see the information about the LOS on that road. A snapshot of the application is shown in Figure 10.
Fig. 8 Speed estimalion results Figure 8 shows an example of the speed estimation results. The speed of each object is displayed on a desktop screen, We compared the estimated speeds with the actual recorded speeds of the subjects taken in the videos. The results have shown Ihat we can obtain speed estimation accuracy of about 88% which we believe is acceptable for this kind of application.
4. EMBEDDED SYSTEM DESIGN In the Section, we present the design of a prototype embedded system which is capable of estimating the traffic LOS using algorithm presented Section III. The block diagram of the system is depicted in Figure 9.
^B .il
Fig. 9 The prototyping system The system consists of a camera connected to an embedded Friendly ARM tiny 6410 platform running at speed of 533 MHz.
The embedded platform is connected to a web
Fig 10 The user interface of the Google map application
Due to the limited speed of the embedded platform, the system can only process the video at a frame rate of about 7 fps. For this frame rate, the system can estimate the LOS at level C, D, E accurately because at these levels the moving speed of vehicles is slow and hence the system can track the vehicles successfully.
However, when the moving speed is fast as at LOS A and B, the system performs poorly. To tackle this issue, we suggest to use another low cost ARM based platform running at a speed of 1 GHz and above such as Dual-core ARM®
Cortex™-A9 based PandaBoard [12].
5. CONCLUSIONS AlVD FUTURE WORK In this paper, we have presented a traffic LOS estimation algorithm and the design of a prototype system for LOS estimation. The algorithm was successfully implemented and tested on PC using functions from OpenCV library. The algorithm was also ported on a low cost ARM based embedded platform. Our future research includes:
TAP CHi KHOA HQC & C6NG NGHf CAC TRITftNC DAI HQC K* THUAT * $6 91 - 2012 Optitnizing the LOS estimation algorithtn ACKNOWLEDGMENT
to mcrease speed when implemented on .^^^ ^^^,^^^^ ^ ^ ^ 1 ^ ,.^^ ,„ , ^ ^ ^ ^^ y^„
embedded systems.
Nam and Do Van Quyen for their help in this Developing applications and services using project
LOS information.
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12. http://pandaboard.org/
Author's address: Pham Ngoc Nam - Tel: 0983608425 - Email: [email protected] Hanoi University of Science and Technology,
No. 1 Dai Co Viet Str., Ha Noi, Viet Nam.
