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Essay: Creation of an automated parking system

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  • Published: 18 March 2024*
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Abstract: The main aim that we have to create this project to completely automated car parking system with minimum human interference. Searching a suitable parking space in popular metropolitan city in extremely difficult for drivers. With the issues of accelerating urban traffic jam and therefore the shortage space, these parking lots are needed to equip. This paper aims to counting the number of cars and identifying the available location.

Introduction

In 21st century finding a free car parking Slot has become a most common problem, especially for those people who travel in the morning to work or for daily routine, they find it highly difficult and challenging to get a parking slot for their cars. The industrial growth of the world is reflected by the increase in the number of automobiles on the streets throughout the world, which has caused a lot of parking related problems.

In our populating cities to find parking space is becoming difficult to traffic increase. Drivers have to go back and looking for parking spaces wasting their time, and fuel consumption.

(I) VARIOUS ALGORITHMS

A. Parking Spot Detection:- Identification of parking spots is done in a scenario where each parking spot is vacant.

1. Parking line dividers identification: Parking lines are identified the image segmenting using eight steps-

• HSV thresholding:-It is done filter for HSV values corresponding to yellow parking line dividers.
• Vertical-coordinate centroid:-the regions are lying in the upper half the image is eliminated.
• Solidity:-the regions with solidity under a threshold are eliminated.
• Area:-small and large regions are eliminated.
• Eccentricity:-those regions with eccentricity under a threshold are eliminated.
• Regions that lie along the same line and whose centroids are within a threshold value are joined to account.
• Vertical-coordinate centroid:-any outlier regions.

• Major axis length:-the regions with major axis length under a threshold are eliminated.

2. Parking spot coordinates identification: Once the parking line dividers have been identified. The four vertices of a quadrilateral represent the endpoints of the neighboring parking line divider. Four more vertices corresponding to the vertices of a 3-d volume that the car occupies are computed by subtracting a height value from base vertex coordinate. This results in an 8-coordinate bounding box that roughly defines the area in the image in which a car would reside. In order to account for occlusion by neighboring cars, which may enter into the bounding box of adjacent bounding boxes, five of these coordinates are chosen to form a bounding box.

Fig1. how the parking space detection algorithm will work

B. Vehicle Detection: This is use of both the image of the empty parking lot that was used to generate the bounding boxes and the coordinates of the bounding boxes themselves.

1) SIFT Detection: The Scale Invariant Feature Transform developed by Dr. David Lowe in 1999, is utilised for detecting and describing local features in images. The car detection algorithm leverages SIFT due to its robustness to image transformations and rotations, ensuring accurate feature identification regardless of the vehicle’s orientation.

While the current scope of the project does not necessitate handling translated or rotated images, SIFT’s versatility makes it well-suited for potential future scenarios where such image adjustments might be encountered. This ensures the system’s adaptability and reliability in diverse conditions.

2) FLANN: Fast Library for Approximate Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest neighbor search in large datasets and for high dimensional features. This was also developed by David Lowe. We matched features based on descriptors but also based off their (x, y) position. Since the camera has not moved between the two images, matched features should be fairly close in pixel proximity to each other.

3) Feature Thresholds: Once we have matched the features from each image to its corresponding image of the empty lot. We utilized the bounding box for each spot that was computed in the first portion of this algorithm to compute the number of features that reside inside.

(II) SYSTEM MODULE

The proposed module on the image processing technique through a MATLAB. This project module involves five step modules to perform operation. The processing step can be shown in the block diagram.

A. System Initialization:-

In the System Initialization Process the manual drawing procedure will be put into practice. In the manual Drawing process the image can be drawn with Park slot number which will be helpful in identifying the Empty parking area. The main objective of this process is to identify the Empty Parking area without any manual interruption. The Diagram drawn should be visible, clear, easy to understand, complete information about the parking slot and it should be sufficient during the process of Initialization. The Sensor and the Camera should be stationary during the initialization plan of the system architecture. Thus the detected image from the camera can be undergone for the further image processing techniques.

Fig 5:- System Initialization

B. Image Acquisition:-

Once the System Initialization module gets completed it can be allowed to the next processing module called as the image acquisition module in the Image Processing Techniques. In the Image Acquisition module the Images can been captured from the parking area through the Camera. The acquired images for the processing can be captured with High definition cameras present at the parking area. The Images can be captured by the Camera by top view and side view of the Parking slot in order to sense the Incoming input image.

C. Image Segmentation:-

Image Segmentation can be a Next type of module present at the Image Processing Steps. The Image Segmentation can be a Major part of the Image Processing technique which can be used to identify and analyze the image at a glance [4]. The process involved in the Image segmentation can be show in the block diagram as shown in figure. 4,

Fig 7:- Flow chart of Image Segmentation Process

The Image Segmentation process will provides the each and every part of the Image. The visual characteristics are obtained by the considering the number of pixels present in that captured image. Thus the Obtained image after segmentation process will be a better quality output result. The obtained set of Pixel can collectively provide the entire image. Thus the Empty parking area in a parking slot can be recognized by the outline, Edge, Boundary, Object, etc. The process involved Image Segmentation is Clustering will partition the Image into number of clusters. The Clustering process can be preferred on the source of assortment of manually or through Random Selection process.

D. Image Enhancement:-

The Image Enhancement module the binary Image obtained from the Image segmentation module is been considered. In this Process the Image has be Enhanced to remove the unwanted noise obtained during the Binary Image Conversion. They can be used to Trace the outline of the Detected Image. The Digital camera will take the Images from various locations with some noise.[9] The Obtained noise can be removed with the help of a technique called Morphology. The Morphology can be a special Technique which is used to neglect the Imperfection obtained during the Image Segmentation[1,7]. The Morphological mechanism undergone the following process named as Dilation, Erosion, Opening and Closing Process and among those four process the Opening and Closing Morphological process are the most commonly used Process for the noise removal. The Opening process is to remove the tiny objects present in the Segmented Image and the Closing process is to remove the unwanted and tiny holes present at the Segmentation process. The main role of the Morphological mechanism is to provide the exact Edge and shape of the image without any Distortion. In the proposed mechanism the exact boundary of the image has to be used to detect the empty parking space is to be traced. The rest of the process named Dilation and Erosion is used in this stage inorder to increase or decrease the pixel range of the Output Image after enhancement. The Dilation is used to improve the Pixel range to the outline boundary of an image. The erosion is an another process which will removes the unwanted pixels on the boundaries. In the proposed process if the input pixel value of an binary image is equal to ‘0’ then the output pixel is to be ‘0’.

E. Image Detection Module:-

The Image Detection Module is implemented only if the Exact Edge and outline Boundaries of an Image is obtained by the Image Enhancement Module. In this process the parameters of Area and Perimeter has to be considered in order to obtain the exact shape of an Image. The exact shape of an Image is necessary to provide information to the drivers to park their vehicles in the empty parking area. The Shape of an Image can be obtained using the below given expression.
Shape = (4×pi×area) / (perimeter^2)

The parking of a vehicle through the image processing can be highly efficient and more accurate without any manual interruption. In the proposed architecture will shows a parking area with 8 slots of parking area. Based on the threshold value the Empty parking area can be displayed through an Camera preview display unit. The display can be indicated through the LED display. In addition to the LED display an Audio system have been interfaced to the System design. Thus the Vehicle is sensed by the Sensor the availability of Parking area and appropriate Parking slot can be informed to the driver through audio announcement. Thus the proposed architecture will be very helpful in park the vehicle in the parking area without any distortion and which results in the time and parking area consumption can be reduced.

(III) RESULT AND DISCUSSION

The proposed parking space vacancy management system was tested to determine its ability to accurately extract vacancy information from captured images of the parking space. A test image was obtained using an 8MP aerially positioned camera. The image was grayed, stretched, smoothed, and binarized by using the image processing techniques described earlier.

The parking of a vehicle through the image processing can be highly efficient and more accurate without any manual interruption. In the proposed architecture will show a parking area with 68 slots of parking area. Based on the threshold value the Empty parking area can be displayed through a Camera preview display unit.

(IV) FUTURE WORK

This application is an initial step in reaching the effective solution for the daily concern. This project can be extended in multiple ways:

• To provide a central management system that make sure only authenticated information is sent to the Client, i.e. dealing with the security issues.
• More analysis can be done using the parking history data by which User can get recommendations or suggestions on parking spaces and their availability trends.
• And this analysis can be used while reserving a parking space by User or while renting a space, to decide the price of the parking space.
• We could also do a mobile application through which driver can get the occupancy statuses of the parking spaces.
• As for the future work the users can book a parking space from a remote location. GPS, reservation facilities and license plate scanner can be included in the future.

2021-8-26-1629977987

Tutor’s feedback:

The project you’ve embarked on addresses one of the most pressing urban challenges today—optimising car parking in densely populated metropolitan areas. The endeavour to automate this process with minimal human interference is not just ambitious but highly relevant given the increasing number of vehicles and the consequent demand for parking space.

Your abstract succinctly outlines the project’s main goal and the challenges it aims to overcome. However, refining it to highlight the specific technological innovations your project brings to the parking system could make it more compelling. For instance, mentioning the use of image processing techniques upfront would immediately attract attention to the technological sophistication of your solution.

In your introduction, you’ve effectively set the scene by presenting the problem statement, although integrating statistical data or findings from recent studies on the parking space shortage could further strengthen your argument. Additionally, a brief overview of existing solutions and their limitations would provide a clearer context for the novelty and necessity of your project.

The detailed breakdown of various algorithms and system modules in your methodology gives a clear insight into the workings of your automated parking system. Your approach to parking spot detection using image segmentation and the identification of parking line dividers shows a thoughtful application of image processing techniques. Furthermore, your incorporation of algorithms such as SIFT and FLANN for vehicle detection demonstrates an understanding of the need for precision and efficiency in such a system.

One area that could be enriched is the discussion on the choice of technology and algorithms. While you’ve described what you used and how, delving into why these particular methods were chosen over others could offer readers more depth. For example, discussing the advantages of using SIFT for feature detection in this specific context would not only reflect a critical evaluation of available techniques but also offer insight into the decision-making process behind the project.

The System Module section carefully outlines the steps involved from system initialisation to image detection, which is commendably detailed. However, it would be beneficial to discuss potential challenges or limitations you anticipate in the practical implementation of these modules. For instance, how does the system cope with varying light conditions, or what measures are in place to ensure the accuracy of car detection in crowded parking lots?

In terms of future work, you’ve hinted at some exciting directions, including the development of a central management system, data analysis for predictive parking, and mobile application integration. Expanding on how you plan to tackle security issues, especially in the context of a central management system, would be particularly interesting. Also, considering the rapid advancements in IoT (Internet of Things), including a discussion on how IoT could be integrated into your project for real-time tracking and management of parking spaces might provide a forward-thinking edge.

In conclusion, your project presents a promising solution to a growing urban issue. The application of sophisticated image processing techniques and the consideration of future expansions show a comprehensive approach. As this field continues to evolve, staying abreast of emerging technologies and continuously iterating on your design will be key to developing a robust and user-friendly automated car parking system.

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