IOT-DRIVEN AUTOMATED OBJECT DETECTION ALGORITHM FOR URBAN SURVEILLANCE SYSTEMS

Dr.S.M.UMA

KINGS COLLEGE OF ENGINEERING,PUNALKULAM.

IJTCSE Research /ISSN 2349–1582 conference publication

Abstract:The Automatic license plate reorganization (ALPR) is one of the solutions of such kind of problem. There is a number of methodologies but it is challenging task as some of the factors like high speed of vehicles, languages of number plate & mostly non-uniform letter on number plate effects a lot in recognition. The license plate recognition (LPR) system have many application like payment of parking fees; toll fee on highway; traffic monitoring system; border security system; signal system etc. In this paper, the different method of license plate recognition is discussed. The systems first detects the vehicle and capture the image then the number plate of vehicle is extracted from the image using image Segmentation optical character recognition technique is used for the character recognition. Then the resulting data is compared with the database record so we come up with the License plate number such as is observed that developed system successfully detects & recognizes the vehicle number plate on real image even when the pixel is of low resolution.

Keywords:

Number Plate Recognition, Gray Processing, Image Acquisition,ImageBinarization, Template Matching.

I.Introduction :

Smart transportation and urban surveillance systems are important internet of things (IoT) applications for smart cities [1][2]. In these smart transportation and urban surveillance applications, cameras/imaging sensors are commonly installed to automatically detect and identify potential vehicles/cars through automated object detection methods. Usually, such automated object detection methods demand high-complexity image/data processing technologies and algorithms. Hence, the design of low-complexity automated object detection algorithms becomes an important topic in urban surveillance systems. Among these researches, both vehicle license plate recognition (VLPR) and vehicle recognition are hot research topics worldwide, which can be applied to many IoT applications, such as road traffic data collection/monitoring, automatic parking charging and access control, and searching stolen vehicles. It is known that a license plate number is a unique identification of a vehicle. Specifically, the license plate recognition, i.e. the extraction of a license plate region from an image, is the key module in a VLPR system [3], which influences the accuracy of the VLPR systems significantly. Different algorithms have been proposed for identifying a vehicle license plate using image processing [4]. One typical way is vertical edge matching [5]. The idea is to first locate the two vertical edges of a license plate, and hence to detect its four corners. In this way, the license plate can be extracted accurately. Using the contrast between the grayscale values, [6] proposed a vertical edge based license plate recognition method. Another technology is morphology based license plate detection. This method is to extract important features of contrast as guidance to search the license plates [7]. In [8], to extract potential text information from the image, a method is proposed using adaptive threshold, fractal filter and morphological analysis. In [9] and [10], edge statistics in combination with morphological approaches are proposed to eliminate the undesired edges in the images. Color based methods are also attempted which make use of the colors of the vehicle license plate. In [11], a color based method combined with the texture characteristics is proposed to try to detect license plate from the color image. In [12] and [13], the combination of edge information and plate color are utilized to identify the vehicle license plates. Based on neural network techniques, other recognition methods of vehicle license plates are proposed. These methods are designed to train classifiers to offer a proper response to the license plate images. In [14], the authors apply genetic algorithm (GA) to the training process and combine the statistic features together with structure features. In [15], a vehicle license plate detection method using neural network approaches is proposed. The proposed scheme utilizes a neural network chip named as CogniMem to detect the vehicle license plates. In [16], the authors propose a method using wavelet transform technique to decompose the images into different layers, and then utilize the low frequency images to combine with neural network technique.

II.Methodology:

Existing System:

The existing system utilizes other technologies like IR reader, smart card or NFC. These technologies are expensive and laborious to implement. The system which uses computer vision to address these problem were using a single central computing server to process the collected images using cameras scattered across the smart city. This in turn clogs the network since it needs to push live video to the server for processing.

Proposed System:

Here we are proposing a unique combination of technologies which were available in the market. We are proposing to do the image processing in the local environment. The images from the camera were processed near the camera itself and the results were published to the central server for further processing. The scope of this project is to present a cost effective viable solutions, so we will be implementing the system and technologies needed to process the image locally and techniques used in detecting the number plate region.

RELATED WORK

Capture of Image:

The first step is the capture of image. The image is captured byelectronic device. Digital Camera or Webcam. The image captured is stored in JPEGformat. Later on it is converted in to gray scale image in MATLAB.

Pre-processing:

The next step after capturing the image is the preprocessing of theimage. When the image is captured there is lot of disturbances and noises present inthe image for which the image can’t be used properly. So in this step the noises from the image are required to be cleared to obtain an accurate result.

a.Gray Processing:

This step involves the conversion of image in to Gray levels.Color images are converted in to Gray image. According to the R, G, B value in the image, it calculates the value of gray value, and obtains the gray image at the same time.

b. Median Filtering:

Media filtering is the step to remove the noises from theimage. Gray level cannot remove the noises. So to make image free from noise media filtering is used.Plate region extraction: The most important stage is the extraction of number plate from eroded image significantly. The extraction can be done by using imagesegmentation method. There are numerous image segmentation methods available in various literatures. In most of the methods image binarization is used.

c. Character segmentation:

In this step get the o/p of extracted number plate using labeling components, and then separate each character and split the each and every character in the number plate image by using split and also find the length of the number plate, then find the correlation and database if both the value is same means it will generate the value 0–9 and A — Z, and finally convert the value to string and display it in edit box, and also store the character in some text file in this code.

SVM Classifier:

• Two class SVM classifier is used to determine whether it is character or not.
• If it is positive then CDF of inner and outer areas are calculated.

Plate localization:

The basic step in recognition of vehicle number plate is to detect the plate size. In general number plates are rectangular in shape. Hence we have to identify the edges of the rectangular plate. Mathematical morphology will be used to detect that region.Sobel edge detector we used to high light regions with a high edge magnitude and high edge alteration are identified. Depending upon the threshold value edge will be detected from the input image. Figure 2.3 shows the input image before applying Sobel edge detection algorithm and figure 2.4 shows after applying the Sobel edge detection method.

Number Plate Extraction:

In this process the plate image will be extracted out of the original vehicle image. The processes helping in doing so is the morphological operators. Here Corner detection will be applied to the image as well as to detect the corners on the identified plate region on the image given.

Feature Extraction:

This process plays an important role towards the recognition in this project and it helps faster the recognition process. This uses the projection analysis method that is used to locate the features that have been analyzed from the vertical and horizontal segmentation. This also involves extracting the characters from the segmented images and these characters are our features the main features that we want to extract out of the number plate in the first place. From this method on will be focusing on single character images that will be easily used for the recognition process.

III. Literature Review

For the recognition the OCR techniques is used whichis susceptible to misalignment and to various sizes. The affine transformation can be used to advance the OCR recognition from various size and angles. The programmed vehicle identification system using vehicle license plate is exhibited. A series of image processing techniques of the system for identifying the vehicle from thedatabase stored in the PC.

In this paper they proposed that Automatic Number Plate Recognition(ANPR) is a method that catches the vehicle image and confirmed their license number. ANPR can be used in the presentation of stolen vehicles. ANPR can be used in various manners by using to identify it stolen vehicleon the highway.They introduced that high level of precision has been required by thenumber plate recognition when streets are occupied and number of vehicles are passing through.In this paper, by optimizing different parameters, they have accomplished an exactness of 98%.

It is essential that for thetracking stolen vehicles and monitoring of vehicles of an exactness of 100% can’t be bargained with. Therefore to accomplish better precision streamlining is required. Additionally, the issues like stains, blurred regions,smudges with various text style and sizes ought to be remembered.

This work can be further boundless tominimize the errors because of them. Automatic recognition of car license plate number got to beindispensible part in our day by day life. \This paper mainly explains an Automatic Number Plate Recognition System (ANPR) using Morphological operations, Histogram manipulation and Edge discovery Techniques forplate localization and characters segmentation. Artificial Neural Networks are used for Character classificationand recognition.

IV.APPLICATIONS OF NPR SYSTEM

1. Parking :

The NPR is used to automatically enterprepaid members and calculate parking fee for nonmembers.

2. Access control :

A gate automatically opens forauthorized members in a secured area, thus replacing orassisting the security guard.

3. Tolling :

The car number is used to calculate the travelfee in a toll-road or used to double check the ticket.

4. Border Security :

The car number is registered in theentry or exits to the country and used to monitor the bordercrossings.

5. Traffic Control :

The vehicles can be directed todifferent lanes according to their entry permits. Thesystem reduces the traffic congestions and number ofattendants.

6. Airport Parking :

In order to reduce ticket frauds ormistakes, the NPR unit is used to capture the number plateand image of the car.

V.CONCLUSION

An efficient less time consuming vehicle number plate detection method is projected which performed on multifaceted image. By using, Sobel edge detection method here detects edges and fills the holes less than 8 pixels only. To removing the license plate we remove connected components less than 1000 pixels. Our anticipated algorithm is mainly based on Indian automobile number plate system. Extraction of number plate accuracy may be increased for low ambient light image.

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