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It is important for the detection of vehicles
to measure the different parameters of traffic in which number and speed of
vehicles is included and it is done under the traffic related applications
which are designed for the protection of life on road. To continue the research
on the traffic parameters, different organizations like Intelligent
Transportation System donate a large amount of funding. There are two criterion
on which identification of different objects takes place and they provide a lot
of information regarding to that object which are texture and features of the
vehicles. Detection of vehicle is classified into two categories on the base of
approaches of appearance based and feature based. To determine the vehicle on
the basis of features include corners and edges of vehicle
Template based matching is also termed as appearance-based
matching of vehicles. In the paper the author described the tracking and
detection of vehicles and started with extraction of colored background such as
by extracting the background of color image during the operation of physical
appearance of vehicles like filling of the hollow object during the opening of
transformation. During the analysis of research, it is the challenging
situation for the researchers while detection and verification of color image
on different patterns and vision of computer
According to the hypothesis of the research, another important
application is identified that verifying the different hurdles and obstacles in
the safety of vehicles under the afety application. The detection of weather
conditions is important factor according to the information from the
meteorological department, most importantly for the traffic management of vehicles
like air, ground and sea. Different detection sensors are used for the
detection of weather conditions such as to check the visibility of the area
used visibility meter, radar, dendrometer and many others. Some systems like
outdoor vision for the recognition, tracking and navigation of weather
conditions which may be bad or not
However, the efficient use of this systems and detectors, in this
ongoing time the systems are unable to detect the common conditions of weather
like mist, fog, snow and rain that also disturbed the movement of vehicles.
Moreover, vision system is developed to identify the all common weather
condition, it is necessary to describe all the weather conditions through
visual effects as well as used the algorithm to minimize these effects.
Variation in the physical characteristics of weather conditions and its visual
effects produce on the image. Furthermore, in graphics system of computer,
heuristic model and particle system is used to provide the information of rain.
As this method to identify the rain is not efficient as it does not describe
the physical properties of rain and unable to develop the visual effect of rain
which is important to described. So, for the removal of haze, deep information
of the degraded image is important parameter. Several methods are present that
describe the extra information and detect the deep information from the images.
Therefore, only binary scattering model is used that take out the information from
the colored images of deep scene under different weather conditions that are
prevailing
The purpose of study is to introduce a
lot of techniques that enhance the image as well as free vision towards the
weather. But the problem is faced while the enhancement of image is that to
reduce the noise while taking the image outdoor. This practice reduced the of
image due to different weather like snow, haze, rain and mist. Moreover, bad
conditions of weather is divided into two categories which are: steady or
static weather conditions in which haze and fog included and other category is
the dynamic weather conditions like snow and rain
The most important technique for the
data mining is the Visual data mining approach. This technique most of the time
based on the computer graphics and in some cases image processing technique
also used. In this paper, image processing method is used which is RNAM
technique. In this technique, visual mining data is proceeding at post process
and as a result image of data mining is produced which is helpful for the
others as they identified the features and different patterns of data
efficiently (Wahab, Su, Zakaria, & salam, 2013).
The bad weather conditions effect the
quality of image and degrade the image and loss of contrast is takes place. In
the application of imaging, this degrade quality of image causes trouble and
difficult to create results. If we take image underwater and under the murky
water, then it is difficult to detect the different artifacts, and this will
reduce the quality of image due to lack of visibility. Moreover, this
degradation of image quality is due to the atmospheric particles that leads to
the scattering and absorption of light. Furthermore, the degradation of road
image created problems for the data recorders, surveillance system of traffic and
intelligent transport system. So, it should be operated by considering the
weather conditions that are prevailing at a specific time in a particular
area.
It is compulsory to remove the dynamic
effects of weather during the video, because for the sake of safe driving. However,
by combining and clustering of GMM and K-means, the pixel wise detection is
improved that provide the removal method of dynamic conditions at the level of
pixel in different time. Moreover, to the detection of dynamic conditions of
weather, a strategy is driven in which transition is occurred for the accuracy
of detection of this condition through the K-means that combining with GMM
Moreover, this method is efficient as
it removes the drops of rain and snow only in image. In this paper, chromatic
technique is used in the removal algorithm in which the color is not loss of
the image. Histogram equalization method is used for the color images that are
applied in different color channels that produce the unwanted result in this
method. For achieving the good result in this method is to transfer the color into
hue, then saturation and color space intensity and then all this applied to equalization
method in this method. As a result, this method does not maintain the color. To
remove the noise in an image is becoming the challenging for the researchers
and remove the noise from image could be minimized by different contributors
according to their different point of view. The methods which are used in
reducing noise are: partial differentiation, different domain methods and
filters of spatial adaptive. Moreover, different strategies are introduced that
are effective and efficient like dictionaries and sparse coding. As by using
the K-SVD training algorithm, the signals of image receive the sparse
decomposition that reduce the effect of other dictionaries. Another method is
used that is novel method in which model is also used for the proper imaging of
snowflakes and rain drops. Guided filers are used that provide the statistical
data for the pixels in which information that detect the rain and snow data
which is needed for the identification
As the problems occurred in this
research, the aim of study is to introduce effective and efficient methods that
reduce the degradation of images under different conditions. However, image
size is greater than the size of epitome and there is need to be developed
epitome in the image
References of Bad Weather Removal for Driving Safety
Bossu·Nicolas, Jérémie, and Hautière·Jean-Philippe
Tarel. 2010. "Rain or Snow Detection in Image Sequences through use of a
Histogramof Orientation of Streaks." International Journal of
Computer Vision .
Garg, Kshitiz, and Shree K. Nayar. 2004. "Detection
and Removal of Rain from Videos." IEEE .
Huang, Shih-Chia, Bo-Hao Chen, and Yi-Jui Cheng.
2014. "An Efficient Visibility Enhancement Algorithm forRoad Scenes
Captured by Intelligent Transportation Systems." IEEE 15 (5).
Xiao, Chunxia, and Jiajia Gan. 2012. "Fast
image dehazing using guided joint bilateral filter." 713–721.
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