Logos Detection from Moving Vehicles

A. Ben Hamida

, M. Brulin

and H. Nicolas

University of Sfax, National School of Engineers, BP 1173, Sfax, 3038, Tunisia

Labri, University of Bordeaux, Talence, France

Keywords: Logos’ Detection, License Plates, Pictograms.

Abstract: To deal with road accidents, especially accidents caused by trucks containing dangerous products, the

possible solution is to control these vehicles’ passage. We aim at developing a software technique

confirming that all the entered engines inside a tunnel are securely quitted, to guarantee that no accidents, no

breakdowns have occurred inside. To implement such solution, we identify the ingoing and outgoing trucks

by extracting their significant marks. These marks help to differentiate each vehicle from the other. They are

the mounted logos as license plates and pictograms. To ensure the safe exit of one truck, we look for the

similarity between the ingoing and outgoing vehicle’s images by comparing their detected symbols. In this

paper, we present a controlling system capable to extract logos from moving trucks to verify their safe

entrees and exits. Both theoretical analyses and experimental results are provided to show the performance

of the proposed system.

1 INTRODUCTION

Most of the accidents happen in goods’ road

transport. At the same time, this sector supports

nearly 75% of the total traffic in tones. In the case of

hazardous product’s transport, in addition to the

usual consequences of transportation’s accidents, can

pop up the effects of the transported product. In fact,

the dangerous materials’ accident combines a

primary effect immediately felt (fire, explosion,

spills), and secondary effects (airborne spread of

toxic fumes, water pollution or soil). Generally, a

hazardous material is a substance that by its physical-

chemical characteristics, toxicological, or the nature

of its reactions can pose risks to the humans or the

environment. In case of accident, it is essential for

emergency services to be rapidly informed about the

nature of the carried products in the immobilized

vehicle. Consequently, a truck, carrying an unsafe

substance, traveling on roads is always characterized

by three types of logos:

-Rectangular plates, with numbers only, mounted

in front and in the back of the vehicle. They are a

30x40 cm plates colored with a reflective orange and

edged with a black border. The upper part, as

mentioned in figure 1(a), provides information about

the detailed characteristics of the material to quickly

evaluate the risks. The lower part presents the

identification number of the product registered as 4-

digits number.

- Diamond-shape symbols, shown in figure 2,

placed behind and on either sides of the vehicle.

Named pictograms, they are various colored panels,

having the shape of a 30 cm edge square. They

report the major hazard of the substance.

-Rectangular plates, with numbers and letters,

mounted in front and in the back of the vehicle.

They are the license plates as shown in figure 1(b).

They can be another characteristic that ameliorates

the vehicle’s unique identification. Almost, it’s

usually a 52x11 cm plate.

So that, all trucks have distinctive marks

informing about the carried product, mounted at the

front and the back. Exploiting these characteristics,

we can extract logos from images taken for moving

trucks while their entrees and their exits from a

tunnel. The context of the existing problem is that

trucks carrying hazardous materials circulating on the

roads. The greatest risk occurs when a truck enters

through a tunnel without living it. The purpose is to

control these trucks and make sure that everyone is

properly coming out. Otherwise, there was certainly

an accident or breakdown that prevented its exit or

delayed it. In this case the emergency services’

intervention is obligatory. As solution, two cameras

are placed one at the entrance, the other at the

613

Ben Hamida A., Brulin M. and Nicolas H..

Logos Detection from Moving Vehicles.

DOI: 10.5220/0004739906130618

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 613-618

ISBN: 978-989-758-004-8

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

tunnel’s exit. They provide videos taken for passing

trucks. The developed system is based on images

extracted from videos, to compare between the

incoming and outgoing vehicles. This paper focuses

on the logos’ detection from moving vehicles.

Figure 1: Example of plates.

Figure 2: Examples of pictograms' models.

Through the literature, we couldn’t find previous

works closely related to our subject. Almost, no one

was interested in detecting logos from moving

vehicle. The majority of the papers are concentrated

separately in license plate recognition and road signs’

extraction. As example, Kiran et al. in (Kiran et al.,

2009), Paulo C et al in (Paulo and Correia, 008)

present a vision based vehicle guidance system able

to detect and recognize traffic signs. These papers

deal with the detection and recognition of traffic

signs from image sequences using the color

information. The authors in (Malik et al., 2010);

(Soheilian et al., 2010); (de la Escalera et al., 1997)

present systems for the detection of road signs from a

road scene image and extracts the pictogram inside

the sign. (Chang et al., 2004); (Rahman et al., 2003);

(Sunghoon et al., 2002); (Megalingam et al., 2010);

(Tamer and Cizmeci, 2009) such as many other

works propose different approaches of license plate

recognition.

2 PROPOSED VEHICLES

CONTROLLING SYSTEM

OVERVIEW

The overall system’s scheme takes as input two

images of the truck (at the entrance and the exit) and

generates as result a decision of similarity (a decisive

score) between the two existing trucks in the pictures.

In what follows, we will discuss the developed

system as presented in figure 3 and explain its stages.

Figure 3: Developed system's stages.

2.1 Regions of Interest Extraction

In our case, the regions of interest are the plates and

the diamond-shaped logos existing in the image. For

this, we have to wonder how the eye can distinguish

a logo in the image’s universe. First of all, color

plays an important part so it will be a decisional

criterion in our technique. Then it is continuity, in

fact, a logo is rarely a set of dispersed pixels but

rather a contiguous area with a specific form. So,

these particular features can be mainly classed in two

types: the color and the shape. Therefore, we narrow

our research in the image’s areas that meet both

criteria. We apply a double analysis to the vehicle’s

image: in the first one, we look for regions satisfying

the color’s criterion. At the second, we extract those

with the required forms.

Regions of interest Extraction based on color

criterion

The logos are quite different from the rest of the

image. Their colors are limited to: saturated red,

saturated orange, saturated yellow, saturated blue...

We can distinguish that they almost vary between the

red and blue colors’ levels. In this case, RGB and

YCrCb colors spaces are efficient for logos’

extraction. YCrCb model provide low distance

between two visually similar colors, while in RGB

model they appear dissimilar. That’s why, our choice

converges to using the red Cr and blue Cb

components of YCrCb color space. Since all the

logos’ colors are saturated, we enhance their

extraction by using the saturation component S of the

HSV model. In other words, we extract areas with

highly saturated colors in the images. We classify

every pixel in the image using this equation:



,

∈





,









,











,









,







With :



,

: Pixel of coordinates  and  of the image .

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

614





, 



, : Red and blue component of

YCrCb model and Saturation component of

HSV model.

, : Red and blue classes.





,



,



: fixed thresholds.

The thresholds are empirically fixed with a large

number of images. Some results are shown in figure

Regions of interest Extraction based on form

criterion

The form is the second decisive criterion in regions

of interest extraction. In plus, the previous step leads

to false negative detections, where it can miss some

logos because of their unsaturated color or for natural

reasons: low luminosity, shadow superimposed, and

plates with spots.... This magnifies the need for a

second analysis based on the form. We can

distinguish that the researched logos forms are

limited to rectangle or rhombus. In this step, we look

for the existing quadrilaterals in the image. We

preprocess the image and find its contours. Then, we

extract the closed contours which:

- Are convex

- Have four sides

- Have four right angles

Some results are presented in figure 5.

Regions of interest Filtering

The major problem of the extraction based on

color criterion step is that the detected regions do not

necessarily have the desired shapes. This detection

leads to false positives that have unlooked-for shapes

or are different from logos. Therefore, we apply an

additional step for the rhombus and rectangles’

extraction from the detected regions (examples of

results are made in Figure 6). We scan the studied

area trying to find the largest rectangle or rhombus

with the highest percentage of the contours coverage.

The surface can be another characteristic for the

regions filtering; a logo is always smaller than a truck

in terms of area. So, all that is detected as too large is

eliminated. As already mentioned in the first part, the

searched logos have normalized dimensions, the

height/width ratio is always invariant. So that, the

size’s ratio is also a discriminant characteristic.

Filtering the detected zones based on area and size’s

ratio allows detecting only the regions most likely to

be logos. At the end of this stage, we obtain the

regions of interest representing the desired distinctive

logos.

As shown in the introduction, the rectangular

plates (orange or license plates) contain always

textual information oppositely to the rhombus logos.

Basing on this assumption, we can enhance our

filtering by adding another characteristic: All

rectangular plates without textual information and all

diamond shaped logos with textual information are

rejected. That’s why; we use an optical character

recognition system (OCR) to extract text from the

logo’s image.

a. Regions of interest Comparison

Each truck going in or out of the tunnel is

characterized by all the logos extracted during the

two first stages. The decision of similarity between

two vehicles is taken by comparing their logos. The

metric for measuring similarity will be the

correspondence between these regions of interest. For

this, we compute the correlation between two input

and output images. We compare each logo from the

first truck’s image with all the others logos from the

second one and select the most similar. The

comparison is based on some criteria. For each

criterion, a score is calculated to finally lead to an

overall score which will decide whether the two

images are representing the same truck or not. The

comparison’s criteria are:

-Matching score 



: To measure the similarity

between two logos, we use the Template Matching

technique. We look for the small areas of an image

that matches a pattern. So we scan the image from

the left top to the bottom right searching for the best

match with the model. At the end, we get a

normalized score expressing the degree of

resemblance.

-Histograms correlation 



: For this metric, we

compare the gray level histograms of both logos’

images and calculate a similarity score.

-Areas ratio of the two logos 



: The logo retains the

same area between the two images. Having an areas’

ratio close to 1, shows the correspondence between

the two.





1

||

,

With A1 and A2 the areas of two images

-Ratio of length / width ratio of the two logos 



: Just

as the areas ratio, the length / width ratio is

maintained. We aim at having a result close to 1 to

ensure the similarity between the two thumbnails.





:1

||

,

With R1 and R2 are the length / width ratio of the

two logos

-Score of belonging to a class 



: The score of

correspondence between two logos is calculated as

LogosDetectionfromMovingVehicles

615

Figure 4: Results of logos detection based on the color criterion.

Figure 5: Results of logos detection based on the form criterion.

Figure 6: Results of extracting rectangles and rhombus from the detected regions.

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

616

follows:





= 1, if logo1 and logo2 are from the same class

(both are plates, license plates, diamond

shaped).

0.5, If Logo1 and Logo2 Have the Same Shape

(One is a license plate and the other is an orange

plate or vice versa)

0, if logo1 and logo2 have different shapes

(one rectangle and one diamond or vice versa)

Going through all the extracted regions of interest,

we calculate a correlation scoreS

,

between each

two. The highest score represents the most similar

thumbnails. It is between 0 and 1 and calculated as

follows:

Comparison scores between two thumbnail

images I and J:



,





S



S



S



S



S





Thus,theoverallscoreofsimilaritybetweentwo

imagesS



is derived by calculating the average of

the best scores.





∑



,



With N: number of best scores obtained

3 RESULTS AND EVALUATION

We evaluate the proposed technique by testing

different images of vehicles carrying hazardous

products. Figure 7 shows some results. We can

notice that almost 95% of the logos are correctly

extracted. The false alarm rate, obtained after the

two extraction processes, reaches nearly 5%. The

detection failure (some examples are shown in figure

8) is mainly caused through the color detection’s or

the form detection’s stage. Some cases are rejected

such as a stretched logo due to the perspective effect

or its small size, lack of lighting in the image, some

logos are confused with the trucks taillights... For

the regions of interest filtering stage, the used

thresholds to decide whether the detected region

have the right surface value or size’s ratio are

empirically fixed. It depends on the camera’s zoom

and its position in the tunnel. For the regions of

interest comparing stage, the Table 1 compares the

correlation’s score between the regions extracted

from a trucks input and output images. The best

correlation’s scores are in bold. The overall score

obtained was 0.92, which proves that the same truck

exists in both images. Note that the maximum scores

obtained correspond to logos visually similar. The

correct identification rate in this stage reaches 90%.

Figure 7: Regions of interest extraction results.

Figure 8: Examples of false negatives results.

LogosDetectionfromMovingVehicles

617

Table 1: Correlation scores of logos extracted from two truck’s images.

0,970

0,442 0,2618 0,359

0,445

0,987

0,3998 0,841

0,336 0,239

0,765

0,191

0,342 0,83 0,338

0,984

4 CONCLUSIONS

In this paper, we propose a logos’ detection from

moving vehicles technique. This system is

developed as solution for safe hazardous products’

transport. Using two images for same truck, at the

tunnel’s entree and exit, we control the vehicle and

make sure it is properly coming out. Otherwise,

there was certainly an accident or breakdown that

prevented its exit or delayed it. This control system

is based on three main stages: regions of interest

extraction, regions of interest filtering and regions of

interest comparing. Through experiments, we

demonstrate that we succeed to achieve high

detection and identification’s rate.

REFERENCES

Kiran, C. G.; Prabhu, L. V.; Abdu, R. V.; Rajeev, K.; ,

"Traffic Sign Detection and Pattern Recognition Using

Support Vector Machine," Advances in Pattern

Recognition, 2009. ICAPR '09. Seventh International

Conference on , vol., no., pp.87-90, 4-6 Feb. 2009

doi: 10.1109/ICAPR.2009.58.

Paulo, C. F.; Correia, P. L., "Traffic Sign Recognition

Based on Pictogram Contours," Image Analysis for

Multimedia Interactive Services, 2008. WIAMIS '08.

Ninth International Workshop on , vol., no., pp.67-70,

7-9 May 2008.

Malik, R.; Nazir, S.; Khurshid, J., "Colour based road sign

detection and extraction from still images," Cybernetic

Intelligent Systems (CIS), 2010 IEEE 9th

International Conference on , vol., no., pp.1-6, 1-2

Sept. 2010.

Soheilian B., A.Arlicot, Paparoditis N., « Extraction de

paneaux de signalisation routière dans les images

couleurs », Actes du 17ème congrès francophone en

Reconnaissance de Formes et Inteligence Artificielle,

RFIA’10, Caen, France, p. 743-750, Janvier, 2010.

de la Escalera, A.; Moreno, L. E.; Salichs, M. A.;

Armingol, J. M.; , "Road traffic sign detection and

classification," Industrial Electronics, IEEE

Transactions on , vol.44, no.6, pp.848-859, Dec 1997.

Shyang-Lih Chang; Li-Shien Chen; Yun-Chung Chung;

Sei-Wan Chen; , "Automatic license plate

recognition," Intelligent Transportation Systems, IEEE

Transactions on , vol.5, no.1, pp. 42- 53, March 2004.

Rahman, C. A.; Badawy, W.; Radmanesh, A.; , "A real

time vehicle's license plate recognition system,"

Proceedings. IEEE Conference on Advanced Video

and Signal Based Surveillance, 2003. , vol., no., pp

163- 166, 21-22 July 2003.

Sunghoon Kim; Daechul Kim; Younbok Ryu;

Gyeonghwan Kim; , "A robust license-plate extraction

method under complex image conditions," Pattern

Recognition, 2002. Proceedings. 16th International

Conference on , vol.3, no., pp. 216- 219 vol.3, 2002.

Megalingam, R. K.; Krishna, P.; Somarajan, P.; Pillai, V.

A.; Hakkim, R. U.; , "Extraction of license plate

region in Automatic License Plate Recognition,"

Mechanical and Electrical Technology (ICMET), 2010

2nd International Conference on , vol., no., pp.496-

501, 10-12 Sept. 2010.

Tamer, E.; Cizmeci, B.; , "A different approach for license

plate recognition system," Signal Processing and

Communications Applications Conference, 2009. SIU

2009. IEEE 17th , vol., no., pp.357-360, 9-11 April

2009.

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

618