SURVEY ON VANISHING POINT DETECTION METHOD FOR GENERAL ROAD REGION...

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SURVEY ON VANISHING POINT DETECTION METHOD

FOR GENERAL ROAD REGION IDENTIFICATION

Karan N. Patel1, Vipul H. Mistry

2

M.E- Research Student, E & C Department, SNPIT & RC, Bardoli, Surat, India1

Assistant Professor, E & C Department, SNPIT & RC, Bardoli, Surat, India2

Abstract: In today’s world automobile industries are coming out with so many new features

in cars. Road infrastructures are also getting much better in all over the world. Due to this,

the number of road accidents has increased and more number of people are dying in it.

Researchers have tried to solve this problem by using virtual driver feature in the car. A

computer vision based road detection and navigation system is put in the car that can help

the driver about upcoming accident and rough driving. But the major problem in

development of this type of system is identification of road using computer. And also

detection of the unstructured roads or structured roads without remarkable boundaries and

marking is a very difficult task for computer. For a given image of any road, that may

difficult to identify clear edges or texture orientation or priori known color, is it possible

for computer to find road? This paper gives answer to that question. First step is to find

vanishing point, which uses Gabor filters to find texture orientation at each pixel and

voting scheme is used to find vanishing point. Second step is to identify boundaries from

this estimated vanishing point. This paper describes the considerable work that has been

done towards vanishing point identification methods so that it can be used in real time for

further applications.

Keywords: Dominant texture orientation, road detection, vanishing point detection,

voting scheme.

I. INTRODUCTION

Over the past few years, there has been lot of research work done to develop

automatic navigation system for unmanned ground vehicles in either structured

environments or unstructured road conditions. One of the difficult parts of the

automatic navigation system is the road detection and also difficult to develop the

system that can find road and non-road regions. Many of the current road detection

system use vision sensors to detect the road. The majority of vision-based road detection

methods are divided into three categories: edge, region, and texture based methods.

Edge-based methods are used for the extraction of road boundaries or road markings.

These methods are work well for structured roads, where the well-painted road

marking or edge boundaries are the important features of the road, but this type of

method do not work well in unstructured environments. However, in unstructured

environments, there are various types of roads like soil, grass, rock, etc. and also with

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different geometric characteristics, lighting, and weather conditions that make it

difficult to identify road region and non-road region [4]. To solve this limitation of

current road detection methods, texture-based techniques have been proposed. To solve

this limitation of current road detection methods, texture-based techniques have been

proposed. In texture-based methods first step is to find local texture orientations and

apply voting scheme to find the locations of vanishing points. A pixel with maximum

votes is considered as the vanishing point of the road. And then direction of the road or

the road boundaries can be extracted by the information of the detected vanishing-point

location [4], [5]. Various methods used so far to extract road boundary using vanishing

point are given in figure.1.

This paper is organized as follows. In section II, unstructured road detection

problem is explained. In section III, detail discussion in vanishing point road detection

methods. In section IV, different boundary extraction methods. In section V. summary

of various road detection methods is given and finally the conclusion.

Figure-1: Classification of vanishing point based road detection method.

II. UNSTRUCTURED ROAD DETECTION PROBLEM

Road detection is a very important step for car without driver to guided vehicles. There

are so many approaches have been proposed in past few years [20-21]. However, this topic

has been already submitted by different researchers, but detection of unstructured road is

interesting task and new challenges due to different environment on the roads [22].

Boundaries of unstructured roads may be not clear and also it may have a low intensity. Thus,

it is difficult task to detect road region because of varying illumination conditions, different

lighting and weather conditions.

Given a single image of any road, that may not be well structured or not have strong

edges or texture orientations, it is very difficult task for computer to detect road region from

given image. All the road detection method depends on road features extracted from road

boundary or road markings or road region properties with road models. Most of the methods

work well for well-structured roads but not work well for unstructured roads. To overcome

this problem some researchers have worked on features like vanishing point. Vanishing point

based road detection method is discussed in next section.

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III. VANISHING POINT BASED ROAD DETECTION

In previous sections we discussed road detection methods which can be classified in three

categories: edge, region and texture based methods. Edge detection based methods reduce the

road detection task to boundary detection or road marking extraction. These methods are

more accurate for structured roads, where well painted road marking or edge boundaries are

the important features. Color cue [23, 24], Hough Transform [25] have been used to find road

boundaries. Region based methods consider that road surfaces which belong to homogeneous

regions like in well-structured roads. Instead of looking for road cues, texture based methods

search for global feature like vanishing point to find road direction [4, 6]. Texture based

methods first find local oriented textures and then makes them vote to find the locations of

the road vanishing points. Estimated vanishing point is very important to determine the

direction of the road.

In 2010, Hui Kong El. Al. [4], presented a general road detection method from a single

image using vanishing point detection approach. The basic steps to find vanishing point based

methods are as follows:

Find texture orientation at each pixel and provided confidence level.

Reduced no of voters which can be used for voting process.

Voting scheme is applied to find vanishing point candidates.

Detect road boundaries from detected vanishing point.

A. Texture Orientation

In most of the vanishing point detection methods texture orientation is detect by using

Gabor Filters because Gabor filters are known to be accurate. For the orientation Ø and a

scale ω, the Gabor kernel is given by [26].

In 2004, C.Rasmussen [4, 6], presented method to find texture orientations in which

Gabor filters is used to perform a Gaussian Fourier transform analysis on the image by

convolution with a set of Gabor kernels parameter like orientation, wavelength, and odd or

even kernels. This method is work well for both the structured and unstructured road. But the

main drawback is that the author used large number of orientations (n=72, n=36) this need to

perform many convolutions per image with such a large kernels is computationally expensive

and increase the complexity and also reducing the speed of algorithm [1, 2].

In 2012, Peyman Moghadam, and Jun Feng Dong Et. Al. [3, 8] presented Optimal Local

Dominant Method with the concept of skyline detection to find the texture orientations. In

outer environment most of the pixels in the upper side of the images are known as sky pixels

or off-road regions. Thus, in order to detect the vanishing-point first step is to remove sky

pixels of the image to increase the speed of system. Once the sky-pixels are removing, apply

Optimal Local Dominant Method which is used to find texture orientations. Author used four

Gabor orientation filters {0, 45, 90, and 135} at each pixel to find texture orientations [8].

Due to the four Gabor orientation filters and skyline detection this method reduced the

complexity and also increases the speed to detect the accurate vanishing-point.

B. Voting Scheme

After finding effective texture orientation at each pixel of image, next step is to apply a

voting scheme to select accurate vanishing-point and detect the road boarders. Different

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authors have been used different voting scheme to detect a vanishing –point like hard voting

scheme, soft voting scheme, locally adaptive soft voting scheme, and multi-dimensions

voting scheme etc. But one major drawback of “hard voting” is that this scheme is do more

favor points that are high in image, leading sometimes to large errors in the estimation of

vanishing point [4].

In 2008, Peyman Moghadam, Janusz A.Starzyk, and W.S. Wijesoma [3], presented new

voting scheme to detect accurate vanishing point. Authors observed that all the dominant

orientations which come either from the edges of the road or from any off-road region have

get the same important on the voting scheme. Therefore, detected vanishing-point location

may be incorrect. Thus, to solve this problem weighted-based Soft Voting Scheme is used,

which assigns weight to each ray drawn along with a dominant orientation. Then apply

weighted ray distance-based voting scheme to overcome the problem of biases toward the

higher pixels in the image. Euclidean distance is used to find a distance. Finally, the pixel

with the maximum supporting rays is selected as the candidate of vanishing-point for the

main part of the road [3, 8].

In 2009, Hui Kong, Jean-Yves Audibert, and Jean Ponce [4, 6] presented Locally

Adaptive Soft Voting Scheme (LASV) to find accurate vanishing-point. Author observed that

in hard voting scheme, the higher image pixels receive more votes than lower image pixels,

which results in wrong vanishing-point detection for road image where the correct vanishing-

point of road is not in the upper part of the image. A locally adaptive soft voting scheme

(LASV) is proposed to overcome this problem. This scheme use local voting region, in which

pixels having low confidence texture orientation estimation are remove. Local voting region

based method produces more accurate detection than the global voting region based method.

For curved road, the vanishing point by our method is more accurate and detected boundaries

are also accurate.

IV BOUNDARY EXTRACTION

After detecting accurate vanishing point, next step is to extract road border by applying

boundary detection method. Different authors have proposed different methods for boundary

extraction like constrained road segmentation, GMM based boundary detection method.

In 2009, Hui Kong, Audibert and J. Ponce [4], presented novel method to find road

border. The accurately estimated vanishing point gives strong information about location of

the road region. Therefore, he proposed a vanishing point based dominant edge detection

method to find the two most dominant edges of the road. Based on the two detected edges,

one can roughly detect the road region and update the vanishing point detected by LASV

with the connected point of two edges. The proposed road detection method find two edges

by initially detecting the first one based on estimated vanishing point and then find second

edge based on the first road border. Author advised not to use only color cue in detecting

these edges because of the following reasons: Color variation with illumination changes. For

some road images, there is very much variation in colors between the road and its

environmental areas, e.g., the road cover with snow or desert road. Or for some roads, color

variation is very fast in the road area. He presented a combined approach of color cue

information and “Orientation Consistency Ratio” (OCR) to identify dominant road borders

from estimated vanishing point.

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In 2012, Penman Moghadam, and Jun Feng Dong [8], presented Rao-Blackwellised

Particle Filter (RBPF) method used to find border of the road. The estimated vanishing point

location can give the direction of road to the vehicle. However, the direct vanishing point

detection from single image is noisy because of the various problems like shadows, road

bumps and vibrations of the camera in the vehicle and also continuously the change in

position of detected vanishing point because of that the road state may change continuously.

However, the direct vanishing point detection from single image is noisy because of the

various problems like shadows, road bumps and vibrations of the camera in the vehicle and

also continuously the change in position of detected vanishing point because of that the road

state may change continuously. To overcome these drawbacks, we consider first to detect the

full vanishing point image. This solution will gives wrong vanishing point results because of

unstructured environments, different backgrounds and shadows false voting candidate. So to

provide more accurate road region, we propose to (RBPF).

IV. SUMMARY OF VARIOUS VANISHING POINT BASED ROAD DETECTION

METHODS

In previous sections various types of vanishing point based road detection methods were

discussed. Table 1 shows the comparison between various popular vanishing point based road

detection approaches proposed so far and discussed in last sections. All the methods are

separated based on types of texture orientation, voting scheme, and boundary extraction

method and remarks.

TABLE I Comparison between Various Vanishing Point Based Road Detection Method

CONCLUSION

After studying different methods which have been proposed for road detection, we can

conclude that features based methods used for road detection i.e. region features and texture

features based method work well for structured roads but fail to give similar accuracy in case

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of unstructured roads. To solve this problem texture properties based methods have been

proposed. By using global information like vanishing point one can find road region for all

type of roads. Any road detection method has to fulfill the following characteristics:

Road detection algorithm should be fully automatic. No human interface should be there.

Speed of algorithm should be fast enough to meet the real time requirements.

Algorithm should work well for both structured and unstructured road and it should be

robust against various conditions like shadows, illumination variations and irregular

structures around roads.

Vanishing point based approach provides a general solution for road detection for all

types of roads and provides a strong cue for road area under effects of shadow and low

illumination environments. Speed is main constraint for vanishing point based road detection

techniques. From the survey, we can conclude that efficiency of this approach mainly

depends on accuracy of texture orientations and no of effective voters that are used for voting

scheme. One can increase the speed of algorithm by reducing no of voters and using only

effective voters during voting process to maintain efficiency of algorithm.

ACKNOWLEDGMENT

This work was supported by respective faculties and classmates. I express sincere thanks to

Mr. J. N. Patel, Chairman Vidya Bharti Trust, Mr. K. N. Patel, Hon. Secretary, Vidyabharti

Trust, Dr. H. R. Patel, Director, Dr. J. A. Shah, Principal, S.N.P.I.T.&R.C.,Umrakh,

Bardoli, Gujarat, India for their motivational & infrastructural supports to carry out this

research. I would like to give special thanks to my guide Prof. Vipul H. Mistry, Assistant

Professor Electronics & Communication Engineering Department of S.N.P.I.T. & R.C.

Umrakh, Bardoli whose timely and persistent guidance has played a key role in making my

work a success.

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