There are several challenges in order to implement this approach for increasing accu-racy the navigation systems. It involved computer vision, image processing, distance measurement approaches and navigation systems. In order to create the relation-ship between these approaches, the deep study is required so that their limitations can be determined. As it involved many approaches and every approach have some advantages and limitations. By considering their limitations it is really hard to create a robust solution. Sufficient results are obtained in the presence of some limitations. Some challenges are observed and study is conducted in order the find the solutions for these solutions. These solutions are given with the help computer vision solution which is already present. These solutions are enough for solving the problems but maybe not sufficient for implementing in real-world problems. In order to make them able to work with real-time problems, large changes required. One of the examples is, Most of the cars contains single front camera. Single camera is
providing sufficient results but for more accurate results can be obtained with the help of stereo cameras but big changes are required in order to implement in the current cars.
3.6.1 Positioning sign at different scales
In my case measuring the size of an object in the real-time with the help, scale is a difficult task. As it is mentioned above, different samples are required from differ-ent distances. In these samples again width of image is computed so that distance measurement can be restricted at these scales. These samples in my approach are taken as shown in figure # 3.12:
Figure 3.12: Capturing of samples with camera.
Samples are captured with a source that is camera, Sample1 is captured at a know distance D1 likewise other samples are taken and stored in the table. Detection is based on this scale data so that distance can measure. This approach is implemented under the implementation section. Equations are given for each part of this figure
# 3.12. This data is not enough for the distance measurement but some camera intrinsic parameters are also considered. In order to understand these properties some background knowledge is added to this section of thesis.
For better understanding about parameters of the camera, Image formation is under explanation. Image formation of an object is not possible without a barrier between image plane and an object. This barrier is called aperture. Aperture is considered an important part of the camera that is used to project any object on the image plane and used for the clarity of the projected image. The aperture has some prop-erties which must be considered, If the aperture is big it shows an image that is not clear and creates blurring effect on the captured image. So for the clarity an image, size of aperture must be reduced in order to clear an image. If size of aperture is reduced unnecessarily there will not enough for image formation but it will result in Diffraction. In our project it is important to understand about these parameters because focal length is concerned that is required in the equation which is used for
distance measurement. Focal length is the distance between aperture and the image plane. Focal length is required in order to control the size of object. Example of focal length is given in the figure # 3.13:
Figure 3.13: Control of focal length and aperture.
3.6.2 Illumination Invariant
Every positioning signs have some properties. With the help of these properties, it is possible to detect and perform recognition on these positions signs. Some-times, detection and recognition are performed on the basis of color and shape. The main point for discussion is to make positioning sign still distinctive when there is changing in Illumination. Change of light in the real-time environmental conditions perform the changes at the intensity level.
Pixels are easy to variant of affine intensity changes that is represented by this equation:
I0(x, y) = αI(x, y) + b (3.3)
In order to make the robust detection and recognition, one could perform 2 steps which are a possible solution for Illumination variant objects.
1. Mean removal on each patch.
2. By performing variance normalization.
Mean removal is performed on the each patch is represented by this equation:
Z(x, y) = I(x, y) − I N
X
x0,y0∈patch
I(x0, y0) (3.4)
Variance normalization is also called unit variance. It is represented by this equation:
ZN (x, y) = Z(x, y) q1
N
P
x0,y0∈patchZ(x0, y0)
(3.5)
It is not only the solution but also most prominent for avoiding illumination to create a simple descriptor as well. Under this approach, the descriptor is created at the pixel level that is very robust to illumination changes. One should put all the pixels of an interested region in a vector and normalize it. Figure 3.14 shows the total functional of a simple descriptor: For normalization of the pixels which are
Figure 3.14: Simple descriptor at pixel level.
already in a vector, one equation is represented:
Wn = W − ¯W
kW − ¯W k (3.6)
The main advantage of this approach, It is robust to illumination but it is easy variable or sensitive to the variation like
• Location.
• Pose.
• Scale.
• Intra-class variability [46].
It performs normalization of that resultant vector can be used in other approaches to solving the different kind of problems.
3.6.3 Scale Invariant
Under my approach, It is very important for the detection to work under the scale variant situation. It has to detect positioning signs at different scales. It creates a se-rious problem for the feature matching. Features in which corresponding is required for some functionality but these features appear at different scales. It will be hard to match them when these appear at different scales. In order to find the solution, scale selection mechanism is required. Scale selection is possible by creating a character scale of an object at different scales. With the help of characteristic scale one can possibly create spatial extent of an object. Character scales of a positioning sign can be seen in figure # 3.15: In order to detect this positioning sign, it creates subsets
Figure 3.15: Characteristic scale of a positioning sign.
of these positioning signs by using this characteristic scale of each sign. Through this matching will be easy for the detection. It chooses an interesting region and compares it with other characteristic scales at which it matches. For selection of this interesting region under this positioning sign one has to make its rotation invariant and under this region there must be a corner. More techniques for making scale invariant feature is Laplacian of Gaussian (LoG), Difference of Gaussian (DoG) and Harris Function.
According to study, Laplacian of Gaussian is the best approach for this kind problem.
The second best approach is Difference of Gaussian (DoG). Laplacian of Gaussian works with the help of Edges and blobs. Actually, Ripples are considered as the edges and blob is where these ripples intersect which produce the superposition.
LoG is the approach in which it finds the scale of a blob that is at interesting region.
It is possible by convolving with Laplacians. It checks at different scales and looks for maximum magnitude of Laplacian is the highest response. Response increases as the scale increases. For scale normalization Derivative of Gaussian filter can be used. At the end of this appraoch, It is all about matching the feature with differ-ent scales in which image is convolved with scale normalization at several scales in
order to match. At different scales laplacian response is measured. Magnitude of Laplacian get higher at the center of blob.
3.6.4 Rotation Invariant
Some descriptors are very sensitive to rotation and some descriptors are robust to the rotation. SIFT is sensitive to rotation but can be by some modifications. Be-cause of some such of limitations, SIFT is not a part of this thesis project. SIFT can be rotation invariant but it will so complicated to implement. In order to make its rotation invariant, there is a solution through dominant orientation. As we know, SIFT is working through patches, If any patch is taken and rotated then whole description of patch is changed. In this case one could search for the dominant ori-entation in the taken patch. When dominant oriori-entation is known and the patch is rotated, this dominant orientation is not going to be changed. This dominant orien-tation is the highest value in the histogram. In the histogram, dominant orienorien-tation is at fixed position then histogram can be shifted. Another solution is possible by using hessian matrix. It is possible before computing descriptor. Even if SIFT is able to perform as rotation invariant, still it can’t be chosen because of it slow in performance already and after adding more constraints it will be slower.
There is another approach that is computing Harris score at the corner where two lines intersect. This approach is based on eigenvectors and eigenvalues. In our posi-tioning sign, corner is taken and its eigenvectors and eigenvalues are known then By turning this corner eigenvectors may change but eigenvalues still remain same. These
Figure 3.16: Example of Rotation invariant corner.
eigenvectors and eigenvalues are computed with the help equation given below:
A = U−1λmin 0 0 λmax
U (3.7)
This approach is robust to rotation but it is scale variant that is negative point in it. But it can be used by combining it with Gaussian pyramid in order to make it scale invariant.
3.6.5 Assigning coordinates to positioning signs at real position
Navigation systems are based on different kind of datasets which are responsible for providing information like routes, landmarks, boundaries, speed limits, images based guidance and positioning signs. Such kind of guidance is based on different kind of datasets. These datasets are providing guidance based on coordinate system.
Coordinate system used for interaction of data within a map through which whole guidance is provided. The coordinate systems are developed to provide location all around the world through developed geographic datasets. Coordinate systems are based on reference systems which enable the working of GPS and other geographic systems.
In our system, location is assigned to the positioning signs with the help of GPS coordinates. These coordinates are the called latitude and longitude to provide a location to an object. Understanding of coordinates system is the basic need of nav-igation systems. These are the coordinates responsible for providing the location to any object in the world.
Figure 3.17: Lines of latitude and Longitude [18].
Horizontal lines are represented as lines of latitude. The Equator is the longest line across the globe is a latitude line. Coordinate assigned to the equator is 0◦ called latitude. Towards the north direction, each line belongs to latitude is increasing by 1◦ to 90◦. Lines towards the north from the equator are denoted with N. Each line will be represented as 50◦N , 60◦N and so on.
From the equator towards the south direction, such lines are also considered as the lines of latitude. Lines from the equator towards the south are also increased by 1◦ to 90◦. As these lines are increasing towards the south direction so these are denoted with S. Horizontal lines towards the south direction from the equator are
represented as 50◦S, 60◦S and so on.
Lines of longitude are known as vertical lines across the globe. Meridian is the central and main line of longitude at position 0◦ longitudes. Moving towards the east each line at the meridian 0◦ is increasing by 1◦ to 180◦. As these lines are moving towards the east direction so these are denoted with letter E. These line moving towards east from the meridian will be represented as 50◦E, 60◦E and so on.
From the meridian towards the west direction, such lines are also considered as the lines of longitude also. Lines from meridian towards the west are also increased by 1◦ to 180◦. As these lines are increasing towards the west direction from merid-ian so these are denoted with W. Vertical lines towards the west direction from the meridian are represented as 50◦W , 60◦W and so on.
A database is required in the type of a XML file in which shows type of positioning at the defined location with the help of latitude and longitude position. This data is created under this master thesis project with the help of a manual GPS device in order to compute the location manually and stored in the file. Each location is checked by a device at the position where this positioning sign available. Few samples are taken so that possibility of whole project can be checked. An example is given in the figure # 3.18:
Figure 3.18: Location of signs by a GPS device.