7/23/2019 Automatic Top-View Transformation for Vehicle Backup Rear-View Camera

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Automatic Top-View Transformation for Vehicle

Backup Rear-View Camera

Vivek MaikDepartment of Electronics and CommunicationThe Oxford College of Engineering, Bangalore,

Indiavivek5681@gmail.com 

Daehee Kim, Hyungtae Kim, Jinho Park, DonggunKim, and Joonki Paik 1

Department of Image

Chung-Ang University, Seoul, Koreawangcho100@gmail.com, ipis.hyungtae@gmail.com,

dkskzmffps@gmail.com, deepain83@gmail.com,

 paikj@cau.ac.kr 

 

 Abstract  — An automatic top-view transformation method is

presented for a vehicle backup rear-view camera. The proposed

method consists of two steps: i) automatic corresponding points

estimation based on the lens specification and ii) view

transformation based on the direct linear transform algorithm.

Major contribution of this work is automatic viewtransformation that is optimized for the vehicle rear view camera

system. The proposed method can be applied to various imaging

systems, such as automotive imaging systems, intelligent

surveillance systems, and vehicle rear view cameras.

 Keywords—View transformation, direct linear transform,

vehicle rear-view camera

I.  I NTRODUCTION 

For the past few years, vehicles have been equipped withvarious cameras for driver safety, convenience, and videoevent recording to provide an extended visual information. Asthe demand of a vehicle vision system increases, the related

research has become popular, such as lane detection [1], videoevent data recording, and inattentive drive monitoring [2].

A vehicle rear-view camera provides users withgeometrically distorted images because of a fisheye lens for awide field-of-view. Drivers are subject to accidents because ofthe unrealistic distance of an object. Existing geometricdistortion correction algorithms required user input for thecorresponding coordinates. However, it is difficult to provide ahighly accurate correction result because of the estimation errorof corresponding point pairs.

In this paper, an automatic top-view transformation methodis presented for a vehicle backup rear-view camera. The proposed method consists of automatic correspondence

estimation using lens specifications and view transformationsusing the direct linear transform (DLT) algorithm.

The coordinate of the target view is estimated using theexternal camera parameters. The three-dimensional (3D) realworld coordinate is first estimated using four corresponding point pairs to calculate the projection transformation matrix.The top-view transformation is then obtained using the projection matrix.

II.  AUTOMATIC CORRESPONDING COORDINATE

ESTIMATION 

At least four corresponding pairs are needed for viewtransformation. The target coordinate is estimated using the

lens specification and the installation information. Fig. 1 showsthe installation information of camera and reference points inthe 3D world coordinate.

Fig. 1.  The installation information of camera and reference points in the 3D

coordinate

The real position of a camera is given by the installationinformation in the vehicle, and the virtual or target position ofthe camera is specified by a user. The reference points in the3D space are not necessarily coplanar. The projected reference points onto the image plane is estimated using the angle of lens.

Angles θ   and ϕ    are first calculated between the camera

 position and a reference point, respectively.

1

1

cos (( ) / ),

tan (( ) / ( )),

c p

c p c p

 z z d 

 y y x x

θ 

ϕ 

−

−

= −

= − −

  (1)

where ( , , )c cc x y z    represents the camera coordinate and d   the

distance between the camera and reference points. Next, the

coordinate of projected points on the image ( , ) I x y  is given as

( / 2) ( / )cos( ),

( / 2) ( / )sin( ),

 I FOV 

 I FOV 

 x w N 

 y h N 

θ ϕ 

θ ϕ 

= +

= −  (2)

IEEE ISCE 2014 1569960647

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7/23/2019 Automatic Top-View Transformation for Vehicle Backup Rear-View Camera

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where w  and h   are the horizontal and vertical sizes of the

image, and  FOV  N    the filed-of-view of the camera.

III.  TOP-VIEW TRANSFORMATION 

The estimated coordinates of the predicted response is usedto generate a transformation matrix defined as

11 12 13

21 22 23

31 32 33

H

h h h

h h h

h h h

=

  (3)

where H is calculated using the DLT algorithm that solves thefollowing linear equations

1

2

3

0 x x h

x 0 x h 0,

x x 0 h

i i i i

i i i i

i i i i

w y

w x

 y x

′ ′ −

′ ′− = ′ ′−

T T T

T T T

T T T

  (4)

where Tx ( , , )i i i i

 x y w=  and Tx ( , , )i i i i

 x y w′ ′ ′ ′=  are the

corresponding pair of the real and virtual positions,respectively. Although there are three equations in (4), onlytwo of them are linearly independent. Since each pointcorrespondence gives two equations, it is possible to solve for

H without the third equation. One may choose 1i

w′ = , which

means that ( , )i i

 x y′ ′  are the coordinates measured in the image.

Finally, the top-view image is generated as

I HIv r =   (5)

where Ir   and Ir   respectively represent the real and virtualviews.

IV.  EXPERIMENTAL R ESULTS 

In order to evaluate performance of the proposed system,we used 720 480×  test images taken by a fisheye lens camera

installed in a vehicle rear view system. The real camera

installation data used in the experiment are 0r 

 x = , 91r 

 y = ,

0r 

 z  = , 35v

θ  =    and 134 FOV  =   .

Fig. 2.  Input test images of a rear-view camera.

The virtual camera setting data used in the experiment are

0r 

 x = , 151r 

 y = , 150r 

 z  = , 90v

θ  =   . Fig 2 shows the result

of proposed method.

(a)

(b)

Fig. 3.  Results of the proposed method, (a) the results of transformation, (b)

the results of crop.

V.  CONCLUSION 

An automatic top-view transformation method was presented for vehicle backup rear-view camera. The proposedmethod is transformed the top-view automatically based on thevirtual camera information by defined an user, so that can be provided a convenience to a driver. The proposed method can be applied to an image transform system, automotive imagesystem, and intelligent surveillance as well as a vehicle rear

view backup camera.

ACKNOWLEDGMENT 

This research was supported by Basic Science ResearchProgram through National Research Foundation (NRF) ofKorea funded by the Ministry of Education, Science andTechnology (2013R1A1A2060470) and by the Ministry ofScience, ICT & Future Planning as Software Grand ChallengeProject (grant no. 14-824-09-003) and by the TechnologyInnovation Program (Development of Super Resolution ImageScaler for 4K UHD) under Grant K10041900.

R EFERENCES 

[1]  R. Danescu and S. Nedevschi, Probabilistic lane tracking in difficultroad scenarios using stereovision,  IEEE Trans. Intell. Transp. syst.,vol. 10, no. 2, pp. 272-282, June 2009.

[2]  Y. Dong, Z. Hu, K. Uchimura, and N. Murayama, Driver inattentionmonitoring system for intelligent vehicles: a review,  IEEE Trans.Intell. Transp. syst., vol. 12, no. 2, pp. 596-614, June 2011.

[3]  R. Hartley and A. Zisserman, Multiple View Geometry in ComputerVision, 2rd ed., Cambridge, 2003, pp. 88–92.

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