Matlab Image processing

1. EXTRACTION OF LICENSE-PLATE NUMBER OF OVER
SPEEDING VEHICLES
Under the guidance of Dr. U.B. Mahadevaswamy
Associate Professor, SJCE, Mysore
Naveen Lamba
Dept. of ECE, SJCE, Mysore
Naveenlmb3@gmail.com
Abstract—Traffic management plays a crucial role in cities
especially in metropolitan. Lots of road mishape occurs due to
over speeding of vehicles. Usually RADAR or LIDAR techniques
are opted for monitoring traffic speed, which deploy active devices
for its operation. Hence lots of extra power is getting wasted. In
this paper, we propose an algorithm to detect the vehicle's speed
using image processing techniques. If the speed exceeds the
prescribed limit, the license plate of the over speeding vehicle is
recognized and forwarded to the concerned authorities.
Key words— Image enhancement, object detection, foreground,
blob analysis, speed, contour, image segmentation and license
plate recognition.
INTRODUCTION
Vehicle speed monitoring is important for enforcing
speed limit laws. It also broadcasts the traffic conditions of
the monitored section of the road of interest. Vehicle
detection and tracking in real time is a challenging task in
traffic surveillance systems. It often acts as an initial step
for further processing such as speed of the detected object
and their license plate recognition[1].
Vehicle detection from a video stream relies heavily on
image processing techniques such as motion detection,
foreground modeling, image enhancement, blob analysis,
centroid calculation and speed calculation. A typical
moving object detection algorithm has the following
feature: (a) Estimation of the foreground (motion
detection[2]) (b) Bounding the detected objects (c)
tracking[3,4] (d) Estimation of velocity.
METHODOLOGY
The approach to create a system to detect the over
speeding vehicles from a video sequence has been put forth
in this section. The block diagram of the proposed system is
shown in Figure 1.
Video input
In the first stage of the project, the videos are captured
using a fixed camera. Video cameras are standard
equipment for modern transportation and management. The
camera is positioned in such a way that vehicles are coming
towards the camera in order to avoid the side views of
movement of vehicles and overlapping of vehicles.
Converting video to image frames
A video sequence is a series of still images with a small
interval between two images. The video sequence is
converted to frames. MATLAB image processing library
convert the video which is in AVI format into frames. It
captures the video stream and stores the frames in the
buffer. This will prepare the image frames for further
processing.
Foreground modeling
The moving objects in the video are to be detected for
further processing. To perform this, foreground modeling is
done to detect the difference between the background and
foreground contents in an image frame. Gaussian Mixture
model[6] is used for foreground detection in the project.
By using the GMM technique, the values of each
particular pixel in the image is modeled as a mixture of
Gaussians. Based on the repetitiveness and variance of each
of the Gaussians of the mixture, Gaussians that correspond
to the background can be determined.
Noise Removal
The recorded video may have some noise due to bad
weather (light, wind, etc.). A morphological operation
called opening is performed to improve the image quality
and to detect moving object.
Foreground
detection
Video input
Speed limit
comparison
Speed
calculation
Image
enhancement
Contour
tracking
License plate
recognition
Fig. 1. System overview

2. The opening is a composite operator, constructed from the
two basic operators erosion and dilation. Opening of set A
by set B, the structuring element is achieved by first the
eroding set A by B, then dilating the resulting set by B.
Visual explanation of the opening process is in Figure 2.
Fig 2. Opening operation.
Contour tracking
Contour tracking tracks the moving vehicles. Moving
vehicles are tracked by bounding contour and updating them
continuously. In the project we use a tool called “Blob
analysis” from MATLAB for contour tracking. A blob is
defined as a region of connected pixels. Blob analysis is the
identification and study of these regions in an image. The
blob features usually calculated are area, perimeter, blob
shape and centroid. Blobs are defined based on the
minimum area. When a moving object with the area greater
than the specified minimum area is detected in the image
frame, it is bounded by a rectangle.
The blobs are given the required shape and color. The
counts of blob present in each of the frames give the vehicle
count in a frame. The same procedure is repeated for the
entire video. To go to the next frame, step command is used
and the predefined function blob is executed for each of the
frames.
Speed Calculation
From the positions of previous processes, which have
already provided us the position of each single vehicle in the
image frame, the centroid coordinates are calculated in each
frame. The speed detection of the vehicle in each image will
be calculated using the position of the vehicle together with
the frame rate of the video.
The algorithm to detect the speed is as follows:
1. Two frames are considered from the video
sequence. They are continuously monitored to
detect the moving vehicles.
2. Once the vehicle is detected at frame 1, it is bound
with the blob and the centroid of the corresponding
vehicle is determined.
3. The centroid of the same blob in the frame 2 is
determined.
4. The Euclidean distance i.e. the pixel length
between the centroid coordinates is calculated and
the time taken to cover the distance is obtained
from the video.
5. The speed is then calculated using the formula:
Speed = K (Distance/ Time) ; K is calibration factor.
License Plate Recognition
The License Plate of the vehicle is recognized if the
speed exceeds the limit. The steps for implementing License
Plate Recognition algorithm in MATLAB are described
below:
1. Conversion of a colored image into gray image.
2. Image is enhanced to improvise the given image by
filling holes, sharpening the edges of objects and
join the broken lines and increases the brightness
of an image.
3. Horizontal and Vertical Edge Processing of the
image. In License Plate Recognition algorithm, the
horizontal and vertical histogram represents the
column-wise and row-wise histogram respectively.
4. The obtained histograms are passed through a low
pass filter to remove the noise present in the image.
5. Segmentation of the image to find all the regions in
an image that has high probability of containing a
license plate.
6. The segmented regions are processed to obtain the
region having highest probability of containing a
license plate.
SIMULATION AND RESULTS
For the implementation of our algorithm we took a video
with a frame rate of 30. Using MATLAB the video was
successfully read and the frames were chosen for the
operations to be performed. Foreground of the frame was
obtained and it was filtered to get clear description of the
boundary of the vehicle. The outcome is as in Figure 3.
Fig 3. A video frame, its foreground and filtered foreground
Using blob analysis, the boundary was provided to the
vehicles and the number of vehicles present in current frame
was displayed on the left top corner. The result is displayed
in Figure 4.

3. Fig 4. Blob analysis and Detection of number of
vehicles on screen.
The speed was calculated as proposed in the algorithm and
successful results were obtained as in Figure 5. When the
speed exceeded the prescribed limit, license plate of the over
speeding vehicle was recognized. The outputs in the
intermediate stages are in Figure 6 and 7. The recognized
license plate can be sent to the concerned authorities.
Fig 5. Display of the measured speed
CONCLUSION AND FUTURE SCOPES
The proposed algorithm detected the moving vehicles
from the video. The images of foreground objects were
enhanced for the smoothening of further processing. The
vehicles were bounded by a blob .The speed of the vehicles
were computed using the proposed algorithm. The license
plate is recognized once the vehicle exceeds the speed limit.
The results were comparable with the true speed of the
vehicles.
Future work will be directed towards achieving the following
issues:
1. Better camera control to enable smooth object
tracking at high zoom, in case video is vibrating.
Video stabilization algorithm is required.
2. Improvement in the algorithm to implement the
system in double lane road.
3. Improvements to be made in the algorithm so that
the system does not fail in the case of different
background and different environmental
conditions.
4. Improvement in MATLAB code for monitoring
and spotting vehicles breaking red light rules.
REFERENCES
[1] Christos-Nikolaos E. Anagnostopoulos, Member, IEEE,
Ioannis E. Anagnostopoulos, Member, IEEE, Ioannis D.
Psoroulas, Vassili Loumos, Member, IEEE, and
Eleftherios Kayafas, Member, IEEE License plate
recognition from still images and video sequences: a
survey. IEEE transactions on intelligent transportation
systems, vol. 9, no. 3, september 2008.
[2] ‘Motion object detection of video based on principal
Component analysis ‘proceedings of the seventh
international conference on machine learning and
cybernetics, kunming, 12-15 july 2008.
[3] Moving object detection tracking system : a real time
implemented, seizième colloque gretsi — 15-19
September 1997 — Grenoble.
[4] ‘Object detection and tracking in video’ Kent State
University, Date: November 2001 .
[5] D. Hari Hara Santosh, P. Venkatesh, P. Poornesh, L.
Narayana Rao, N. Arun Kumar Tracking Multiple
Moving Objects Using Gaussian Mixture Model
International Journal of Soft Computing an Engg.
(IJSCE) ISSN: 2231-2307, Vol-3, Issue-2, May 2013.
[6] Naikur Bharatkuma r Gohil, "CAR LICENSE PLATE
DETECTION",Dharmsinh Desai University,India,2006.
Fig 6. Sharpened gray image
Fig 8. Recognized license plate
Fig 7. Segmented image